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WO2019100354A1 - Procédé de détection d'état et appareil associé - Google Patents

Procédé de détection d'état et appareil associé Download PDF

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Publication number
WO2019100354A1
WO2019100354A1 PCT/CN2017/112981 CN2017112981W WO2019100354A1 WO 2019100354 A1 WO2019100354 A1 WO 2019100354A1 CN 2017112981 W CN2017112981 W CN 2017112981W WO 2019100354 A1 WO2019100354 A1 WO 2019100354A1
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Prior art keywords
point cloud
cloud data
historical
frame point
current frame
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Ceased
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PCT/CN2017/112981
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English (en)
Chinese (zh)
Inventor
丘志宏
朱望斌
刘传建
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Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
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Priority to CN201780096950.0A priority Critical patent/CN111373336B/zh
Priority to PCT/CN2017/112981 priority patent/WO2019100354A1/fr
Publication of WO2019100354A1 publication Critical patent/WO2019100354A1/fr
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions

Definitions

  • the present application relates to the field of positioning technologies, and in particular, to a state sensing method and related devices.
  • SLAM Simultaneous Localization and Mapping
  • SLAM For SLAM technology, error accumulation is a major technical problem.
  • SLAM typically uses Kalman filtering and loopback detection to reduce error accumulation.
  • Kalman filter is an algorithm that uses the linear system state equation to estimate the system state through the input and output of the system.
  • the Kalman filter uses the probability to represent the system state. The error is positive. State distribution or other linear probability distribution.
  • Loop detection is also called loop closure detection.
  • the autonomous vehicle or other autonomous mobile device performs loopback motion, that is, the starting point and the ending point are basically the same, then the Bayesian probability map can be optimized for the previously estimated path. To make the entire path estimate more accurate.
  • the embodiment of the invention provides a state sensing method and related equipment, which can filter irregular noise and improve the positioning precision of the SLAM system without requiring loopback motion.
  • an embodiment of the present invention provides a state sensing method, where the method includes:
  • Collecting current frame point cloud data extracting N historical frame point cloud data from the historical point cloud library, the collection time of the N historical frames is before the acquisition time of the current frame, where N is greater than or equal to 1 a positive integer; the current frame point cloud data and the N historical frame point cloud data are respectively subjected to point cloud registration to obtain N state estimation values of the current frame point cloud data; and the N states are The estimated values are superimposed to obtain a state estimation result of the current frame point cloud data.
  • the noise is generally a high frequency, short time shock signal.
  • the SLAM system disposed in the vehicle may be applied.
  • the method for sensing the current state of the vehicle by the SLAM system is: after collecting the current frame point cloud data, the current point cloud data is compared with the previous frame point cloud data collected. The cloud is registered to obtain a plurality of state estimates, which are locations and/or poses corresponding to different historical frame point cloud data.
  • Each state estimate from point cloud registration has a phase
  • the reliability of the response should be low if the noise is affected, and the reliability is low if it is less affected by the noise.
  • a plurality of state estimation values are selected for superposition to obtain a current state estimation result, and the reliability of the state estimation result is relatively high, that is, the result is relatively accurate.
  • the reliability of the result obtained after superposition is higher.
  • the state estimation value includes an expected value and a weight value; wherein the weight value is determined by a coincidence degree value of the current frame point cloud data and the historical frame point cloud data.
  • the weight value is represented by a linear distribution; superimposing the N state estimation values to obtain a state estimation result of the current frame point cloud data, comprising: basing the expected value of the N state estimation values based on And performing weighted superposition on the corresponding weight value to obtain a state estimation result of the current frame point cloud data.
  • the two frames of images have different degrees of coincidence based on whether there is noise and the intensity of the noise. If the two frames of images are completely accurate and have no noise, then the overlapping portions of the two frames of images are completely coincident after conventional processing such as translation, rotation, etc.; if the current frame point cloud data and/or historical frame point cloud data are noisy, Then the overlapping portions of the two frames of images cannot completely coincide.
  • the degree of overlap of the overlapping parts of the two frames of images. Therefore, the degree of coincidence of the two frames of images will represent the credibility of the expected value of the state estimate. The higher the degree of coincidence, the higher the degree of credibility, the lower the degree of coincidence, and the more credible. low.
  • the degree of coincidence can be quantized into a weighted value of a linear distribution such as a variance or a standard deviation or a covariance. That is, the magnitude of these variances or standard deviations or covariances determines the weight values of the corresponding state estimates.
  • a linear distribution such as a variance or a standard deviation or a covariance. That is, the magnitude of these variances or standard deviations or covariances determines the weight values of the corresponding state estimates.
  • the larger the irregular noise the smaller the weight value of the obtained state estimation value; the smaller the irregular noise, the larger the weight value of the obtained state estimation value.
  • the embodiment of the present invention can filter the noise in real time to obtain a more accurate state estimation result.
  • the image acquired when the ridge is overlaid will be blurred, and the error of the result obtained by the point cloud registration based on these images will be large.
  • the images acquired before the hurdle are clear, and the error of the result of point cloud registration based on these images will be small.
  • the current frame is a post-cannon image
  • the image acquired after the ridge is taken out and the previous image of several frames are respectively subjected to point cloud registration, and the weight value of the result obtained by the blurred image is small, and the weight value of the clear image is large, then After the weighted superposition of the registration results, the obtained state estimation results are more accurate.
  • the weight value of each estimated state value in the superposition process may be further adjusted according to the type, size, and the like of the noise.
  • the current frame point cloud data is saved to the historical point cloud database as the historical frame point cloud data, the next time the point cloud data is collected, the above process is repeated and iteratively, and the effect of eliminating noise in real time can be achieved.
  • the mathematical form of the state estimation value may be a probability distribution estimation, a variance, a covariance, a covariance matrix, a non-probability distribution estimation, etc., and then the state estimation values are superposed. Result
  • the mathematical form remains the same.
  • the mathematical form of the state estimate is a normal distribution (including the expected value, and the variance as the weight value), the two normal distributions can be superimposed, and the result obtained after the superposition is still a normal distribution.
  • the method before extracting N historical frame point cloud data from the historical point cloud database, the method includes: detecting random noise; extracting N history from the historical point cloud library The frame point cloud data includes: triggering extracting N historical frame point cloud data from the historical point cloud library in the case that the irregular noise is detected.
  • the system detects whether there is irregular noise in the current data frame currently collected, and if it detects that there is irregular noise in the current data frame, it may extract some historical frame point cloud data before the current data frame. . If no irregular noise is detected in the current data frame, the historical frame point cloud data in the previous frame of the current data frame may be extracted.
  • the system detects in real time whether there is a historical frame with noise in the historical point cloud library, and if a historical point cloud library is detected, there is a historical frame (normal data) that is not affected by noise and A historical frame affected by noise may extract a number of historical frame point cloud data preceding the current data frame. If no historical point cloud inventory is detected in the random noise, the historical frame point cloud data in the previous frame of the current data frame may be extracted.
  • the extracting N historical frame point cloud data from the historical point cloud database includes: determining a sampling step size according to the random noise; and calculating a history based on the sampling step N historical frame point cloud data is extracted from the point cloud library.
  • the system determines a sampling step size according to the random noise when the random noise is detected, and extracts, before the point cloud data, from the historical point cloud database based on the sampling step size.
  • Several historical frame point cloud data For example, the system detects that there is random noise in the k+1th frame to the nth frame in the historical point cloud library, determines the sampling step size as n frames, ignores the history frame with noise, and extracts the history before the noise.
  • a frame (such as the first frame to the kth frame in the illustration) is used as the point cloud data set.
  • only one historical frame that is not affected by the random noise can be extracted based on the sampling step size to partially achieve the effect of filtering out noise of different frequencies in the embodiment of the present invention.
  • the image captured during the ridge will be blurred, if the two frames before and after the ridge are extracted, because the two frames The images are all clear, so the results of point cloud registration for the steps described below will also be accurate. In this way, the error caused by the random noise when the ridge is over, will not spread to the measurement after the ridge.
  • the extracting the N historical frame point cloud data from the historical point cloud database includes: detecting that the current frame point cloud data collection order is in a preset order; Extracting the point cloud data set in the point cloud library before the current frame point cloud data, including: triggering from the historical point cloud database when detecting that the current frame point cloud data collection order is in a preset order Extract N historical frame point cloud data.
  • the system detects that the order of the current frame point cloud data conforms to the preset order, extracting a plurality of historical frame point cloud data before the current frame point cloud data. If the system detects that the order of the current frame point cloud data does not conform to the preset order, the previous frame of the current frame point cloud data may be extracted as the point cloud data set.
  • the acquisition order of the current frame is the 5th frame, the 10th frame, the 15th frame, etc.
  • the first 4 frames of the historical frame point cloud data of the current frame point cloud data are extracted as the point cloud data set
  • the historical frame point cloud data of the previous frame of the current data frame may be extracted as the point cloud data set.
  • an embodiment of the present invention provides a device for state awareness, where the device includes an acquisition module, an extraction module, a registration module, and a superposition module, where:
  • An acquisition module configured to collect current frame point cloud data
  • an extraction module configured to extract N historical frame point cloud data from the historical point cloud library, where the collection time of the N historical frames is before the acquisition time of the current frame, where N is greater than or equal to 1 Integer
  • a registration module configured to perform point cloud registration on the current frame point cloud data and the N historical frame point cloud data respectively, to obtain a plurality of state estimation values of the current frame point cloud data
  • a superimposing module configured to superimpose the N state estimation values to obtain a state estimation result of the current frame point cloud data.
  • the acquisition module, the extraction module, the registration module, and the overlay module can be used to implement the method of the first aspect.
  • an embodiment of the present invention provides a device for state awareness, the device comprising: a processor, and a sensor and a memory coupled to the processor, wherein the sensor is configured to acquire a current frame point Cloud data; the memory is for storing a historical point cloud library and program code; the processor is configured to invoke the program code to perform the method of the first aspect.
  • the embodiment of the present invention provides a computer readable storage medium for storing an implementation code of the method of the first aspect.
  • an embodiment of the present invention provides a computer software product, which when used in a computer, can be used to implement the method described in the first aspect.
  • the object of the SLAM system performs point cloud registration through the current frame point cloud data and several historical frame point cloud data during normal driving, and obtains several states about the current position or posture.
  • the estimated values are superimposed on these state estimates, and the irregular noise is eliminated by the algorithm during the superposition process.
  • the sensor sampling frequency is usually high.
  • the traditional Kalman filter if the sampling frequency is high, it is susceptible to random noise, resulting in large errors in the results.
  • FIG. 1 is a schematic structural diagram of a system of a SLAM system according to an embodiment of the present invention
  • FIG. 2 is a logic block diagram of a function implementation of a SLAM system according to an embodiment of the present invention
  • FIG. 3 is a schematic flowchart of a state sensing method according to an embodiment of the present invention.
  • FIG. 4 is a logic block diagram of an application state sensing method scenario according to an embodiment of the present invention.
  • FIG. 5 is a schematic flowchart diagram of still another state sensing method according to an embodiment of the present disclosure.
  • FIG. 6 is a schematic diagram of a scenario for extracting a history frame according to an embodiment of the present invention.
  • FIG. 7 is a schematic diagram of another scenario for extracting a history frame according to an embodiment of the present invention.
  • FIG. 7b is a schematic diagram of another scenario for extracting a history frame according to an embodiment of the present disclosure.
  • FIG. 7c is a schematic diagram of another scenario for extracting a history frame according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic diagram of another scenario for extracting a history frame according to an embodiment of the present disclosure.
  • FIG. 9 is a schematic diagram of another scenario for extracting a history frame according to an embodiment of the present disclosure.
  • FIG. 10 is a schematic diagram of a scenario in which state estimation values are superimposed according to an embodiment of the present invention.
  • Figure 11a is a schematic diagram showing the results obtained by a conventional scheme in a comparative experiment.
  • Figure 11b is a schematic view showing the results obtained by the embodiment of the present invention in a comparative test
  • Figure 12a is a schematic diagram showing the results obtained by another conventional scheme in a comparative experiment.
  • Fig. 12b is a schematic view showing the results obtained by the embodiment of the present invention in a comparative test.
  • FIG. 13 is a schematic structural diagram of a SLAM terminal according to an embodiment of the present invention.
  • FIG. 14 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
  • the embodiment of the invention provides a SLAM system, which can be applied to an automatic driving object such as a driverless car or a mobile robot.
  • Figure 1 shows a scenario in which a SLAM system is applied to a driverless car.
  • the SLAM system can include an inductor, a memory, and a processor.
  • the sensor is used for data acquisition
  • the memory is used for data storage (such as storing data of the historical point cloud library)
  • the processor is used for data processing.
  • the SLAM system may include several process modules, such as data acquisition, extended visual odometer, extended back-end optimization, and construction.
  • the embodiment of the present invention mainly focuses on visual odometer and back-end optimization. These two modules have been extended and optimized. After the sensor completes the data acquisition, the processor is performing an extended visual odometer, extended backend optimization, and mapping.
  • the specific instructions are as follows:
  • the senor is used for data acquisition, and the sensor may be, for example, a laser radar, a 3D camera, a GPS, a speedometer, an accelerometer, an odometer, etc., and the data is data related to the driving state.
  • the sensor is a 3D camera or a laser radar.
  • the 3D camera or the laser radar monitors the surrounding environment in real time to obtain a stereoscopic image of one frame (ie, spatial point cloud data).
  • the sensor can also be a GPS, a speedometer, an accelerometer, an odometer, etc., and then the running state information (such as speed, acceleration, distance, etc.) of the car can be obtained according to the sensor.
  • visual odometry is to perform two-frame images before and after processing, and perform point cloud registration on two images before and after, to estimate the relative motion of the automatic driving object at two moments, and determine the automatic driving.
  • the position and/or posture of the object In the embodiment of the present invention, the function of the visual odometer is expanded.
  • the point cloud data of the current frame acquired in real time hereinafter referred to as the current frame point cloud data
  • the previously acquired history are obtained.
  • the point cloud data of the frame hereinafter referred to as historical frame point cloud data
  • the correlation means that part of the image content in the frame overlaps, and the movement of the automatic driving object in two frames can be estimated according to the overlapping portion of the image. Therefore, the current frame point cloud data and the plurality of historical frame point cloud data are respectively subjected to point cloud registration, thereby obtaining a plurality of state estimation values, wherein the state estimation values are obtained corresponding to different historical frame point cloud data. Position and / or posture.
  • point cloud registration also known as point cloud matching, refers to a technique in which two or more point cloud data are best fitted according to common features or markers.
  • each state estimation value corresponds to a result reliability.
  • the auto-driving object causes strong jolting of the vehicle in a short time, which is reflected in the point cloud data that encounters irregular noise (referred to as noise).
  • noise irregular noise
  • random noise is generally a high frequency, short time shock signal. Based on the presence or absence of noise and the intensity of the noise, these state estimates will have different degrees of confidence.
  • the degree of overlap of the overlapping portions of the two frames of images so the degree of coincidence of the two frames of images will represent the credibility of the state estimate, the higher the degree of coincidence, the higher the degree of credibility, the lower the degree of coincidence, and the lower the credibility.
  • the degree of coincidence can be quantized into a linear distribution such as variance or standard deviation or covariance.
  • the magnitude of these variances or standard deviations or covariances determines the weight values of the corresponding state estimates.
  • the larger the irregular noise the smaller the weight value of the obtained state estimation value; the smaller the irregular noise, the larger the weight value of the obtained state estimation value. In this way, after a plurality of state estimation values are superimposed, random noise can be filtered.
  • the state estimation value includes an expected value and a weight value; wherein the weight value is determined by a coincidence degree value of the current frame point cloud data and the historical frame point cloud data; the weight value is linear And superimposing the N state estimation values to obtain a state estimation result of the current frame point cloud data, including: performing the expected value of the N state estimation values based on the corresponding weight value The weighted superposition obtains a state estimation result of the current frame point cloud data.
  • the current car has just passed a hurdle, and there is a big bump, which causes the first five frames of the current frame to be affected by random noise. Then, after the current frame and the first five frames are respectively registered by the point cloud, the result is obtained.
  • the state estimate has low confidence and the corresponding weight value is low. If the current frame is the point cloud data after the ridge, and the first frame of the current frame is the point cloud data before the ridge, the state estimation value obtained by the point cloud registration according to the two frames is highly reliable, and the corresponding weight is The value is high. By weighting the results of the plurality of state estimation values in this way, the effect of eliminating random noise can be achieved.
  • the state estimation value may be saved in the historical point cloud library, and the iterative calculation is continuously performed during the motion described later, and the embodiment of the present invention is repeated.
  • the current running state information can be sensed, and the mapping process can be performed in real time.
  • the two frames of images are connected in turn, and finally the global stereo image is obtained, and the map built by SLAM is obtained.
  • the embodiment of the present invention does not specifically limit the mapping process. It can be understood that in the latter application, the SLAM system can match the current frame obtained in real time with the built map described above, and can locate the position of the automatic driving object in the map.
  • the method includes but is not limited to the following steps:
  • Step S101 Collect current frame point cloud data.
  • the current frame point cloud data is the currently collected point cloud data.
  • the sensor includes at least one of an optical sensor (such as a 3D camera), a laser sensor (such as a laser radar), a speedometer, an accelerometer, and an odometer.
  • an optical sensor such as a 3D camera
  • a laser sensor such as a laser radar
  • a speedometer for example, a self-driving car periodically photographs the surrounding environment through a 3D camera or a laser radar to obtain point cloud data of one frame and one frame.
  • the point cloud data refers to a set of scanning points in a three-dimensional coordinate system, the scanning points usually exist in the form of a set of vectors, which usually represent the monitored (scanned) objects in the form of three-dimensional coordinates. Geometric position information.
  • the point cloud data may also represent information such as RGB color, gray value, depth, segmentation result, and the like of the scanning point.
  • Step S102 Extract a point cloud data set before the current frame point cloud data from the historical point cloud library.
  • FIG. 4 is a block diagram of a state sensing method according to an embodiment of the present invention. As shown in FIG. 4, after acquiring current frame point cloud data through a sensor in real time, on the one hand, the current frame point cloud may be Data is saved to the historical point cloud library; on the other hand, a point cloud data set before the current frame point cloud data is extracted from the historical point cloud library, the point cloud data set including one or more history Frame point cloud data.
  • the historical point cloud library dynamically maintains the saved point cloud data, that is, the historical point cloud library dynamically stores a plurality of recently obtained point cloud data, and will exceed The point cloud data of the deadline is deleted.
  • the point cloud data set may be part or all of point cloud data in the historical point cloud library. For example, only the last 20 captured images are saved in the historical point cloud library, and the images before 20 frames are deleted, and the extracted point cloud data sets are 10 frames of images in the historical point cloud library.
  • the state estimate is used to describe the location or pose of the object in which the SLAM system is located, for example, the state estimate is an estimate of the current location of the self-driving car.
  • the mathematical form of the state estimation value may be a linear distribution of probability distribution estimation (such as normal distribution), variance, covariance, covariance matrix, non-probability distribution estimation, and the like.
  • the point cloud data and each of the historical frame point cloud data are respectively subjected to point cloud registration, and the obtained result includes: a plurality of current state estimation values of the object of the SLAM system obtained based on the point cloud matching. And determining, according to the coincidence degree value of the point cloud data and each of the historical frame point cloud data, a weight value of each of the state estimation values.
  • the mathematical form of the weight value may be a linear distribution of probability distribution estimates (eg, normal distribution), variance, covariance, covariance matrices, non-probability distribution estimates, and the like.
  • the point cloud registration is an operation of shifting, rotating, etc. of the image, so that the same scanning points on the two frames of images can be aligned one by one, and the state change of the object of the SLAM system during the two frames of images is determined, such as the current frame ratio. How many meters are advanced in the history frame, how many angles are turned, etc., to calculate state estimation values such as the current position or posture.
  • the extracted point cloud data set includes historical frame data 1, historical frame data 2, and historical frame data 3.
  • the current frame point cloud data is respectively associated with historical frame data 1, historical frame data 2, and historical frame data.
  • 3 Perform point cloud registration to obtain state estimation value 1 and weight value 1, state estimation value 2 and weight value 2, state estimation value 3 and weight value 3, respectively.
  • the degree of confidence corresponding to the state estimate can be calculated based on the number of features that can be registered and the number of features on the unregistered. The fewer the number of features on the match, the lower the confidence of the state estimate, and the greater the number of features on the match. The higher the credibility of the state estimate.
  • the credibility may be represented by a weight value
  • the mathematical form of the weight value may be a variance or a standard deviation or a covariance, and may be a probability distribution, such as a normal distribution. It can be understood that, in the embodiment of the present invention, the registration result is capable of sensing noise. If the noise is large, then more registration of the features will be performed when the point cloud is registered, and the corresponding calculated weight value is also lower. .
  • the plurality of state estimation values are superimposed together to obtain a total state estimation value about the object where the SLAM is located.
  • the mathematical form of the state estimation value may be a probability distribution estimation, a variance, a covariance, a covariance matrix, a non-probability distribution estimation, etc.
  • the mathematical form of the result obtained by superimposing the state estimation values is unchanged.
  • the mathematical form of the state estimate is a normal distribution, and the two normal distributions can be superimposed, and the result obtained after the superposition is still a normal distribution.
  • the result of the point cloud registration includes a weight value in addition to the state estimate. Then, each state estimation value needs to be weighted and superimposed based on the corresponding weight value, thereby obtaining a state estimation result of the current frame point cloud data.
  • the point cloud registration process can sense random noise, and the weight value calculated based on different historical frame point cloud data is negatively correlated with the random noise, and after the superposition, the obtained state estimation result can be correspondingly Reduce the effects of random noise.
  • the weight value of each estimated state value in the superposition process may also be adjusted according to the type, size, and the like of the noise. The larger the noise, the smaller the corresponding weight value is adjusted, and the noise is further eliminated, and the current frame can be guaranteed to be processed. When the cloud data is clicked, the resulting state estimation result is relatively accurate.
  • the object of the SLAM system performs point cloud registration through the current frame point cloud data and several historical frame point cloud data during normal driving, and obtains several states about the current position or posture.
  • the estimated values are superimposed on these state estimates, and the irregular noise is eliminated by the algorithm during the superposition process.
  • the sensor sampling frequency is usually high.
  • the traditional Kalman filter if the sampling frequency is high, it is susceptible to random noise, resulting in large errors in the results.
  • the method includes but is not limited to the following steps:
  • Step S201 Collect current frame point cloud data.
  • the point cloud data may be collected by an inductor; the sensor includes at least one of an optical sensor (such as a 3D camera), a laser sensor (such as a laser radar), a speedometer, an accelerometer, and an odometer.
  • an optical sensor such as a 3D camera
  • a laser sensor such as a laser radar
  • Step S202 Detect whether the current triggering policy is met, and if yes, trigger step S203.
  • the triggering strategy is: in the case that detecting that there is random noise in the current frame point cloud data, triggering to perform the subsequent step S203. For example, when the current frame point cloud data is collected, the current frame point cloud data is first registered with the point cloud data of the previous frame point, and the degree of coincidence of the two frames is used to determine whether there is noise. If the degree of coincidence is low, There is random noise, thereby triggering the execution of the subsequent step S203.
  • the triggering strategy is: in the case that abnormal noise is detected in the historical point cloud library, triggering to perform the subsequent step S203.
  • historical point cloud data is maintained in the historical point cloud library, and the historical frame point cloud data corresponds to the weight value, and the smaller weight value indicates that the reliability is low, that is, the history.
  • Frame point cloud data is affected by noise. Therefore, each time the current frame history data is collected, the history point cloud database is first detected whether there is noise-affected historical frame point cloud data. If it exists, it indicates that random noise is detected, and the subsequent step S203 is triggered.
  • the triggering strategy is: triggering to perform the subsequent step S203 in the case that the environment is detected by the sensor to easily cause random noise. For example, when the sensor detects the state of motion of the vehicle, when the sensor detects that the vehicle is significantly bumpy on the uneven road surface, it can be determined that the environment is likely to cause irregular noise, and the subsequent step S203 is triggered.
  • the triggering policy is: when detecting that the order of the current frame point cloud data conforms to the preset order, triggering to perform the subsequent step S203.
  • each point cloud data corresponds to an order number.
  • the order of the current frame point cloud data satisfies the periodic order, if the current frame is the 5th frame, the 10th frame, the 15th frame, ... Step S203.
  • the detection does not meet the preset triggering strategy, it indicates that there is no noise or the noise is weak at this time.
  • the technical solution of the present invention may not be executed, and the current frame point cloud data is directly compared with the previous frame. Registration and completion of back-end graph optimization, mapping positioning and other work.
  • Step S203 Extract a point cloud data set before the point cloud data from the historical point cloud library.
  • a historical point cloud library is set in the SLAM system, and the historical point cloud library is used to save the point cloud data collected recently, and dynamically maintain the saved point cloud data.
  • k frame historical frame point cloud data before the current frame is extracted in the historical point cloud library as the point cloud data set.
  • k 10
  • k 10
  • 10 frames of historical frame point cloud data before the current frame is fixedly extracted as the point cloud data set.
  • the system detects in real time whether there is random noise in the current data frame currently collected. Referring to FIG. 7a, if it is detected that there is random noise in the current data frame, the k frame before the current data frame is extracted. Historical frame point cloud data is used as the set of point cloud data. Referring to FIG. 7c, if no irregular noise is detected in the current data frame, the previous frame historical frame point cloud data in the current data frame is extracted as the point cloud data set.
  • the system detects in real time whether there is a historical frame with noise in the historical point cloud library.
  • a historical point cloud library if a historical point cloud library is detected, there is a historical frame that is not affected by noise (normal The data and the historical frame affected by the noise (the k+1th frame in the figure) extract the k-frame historical frame point cloud data before the current data frame as the point cloud data set. If no historical noise is detected in the historical point cloud inventory, the previous frame historical frame point cloud data in the current data frame is extracted as the point cloud data set (also shown in Figure 7c).
  • the system determines a sampling step size according to the random noise when the random noise is detected, and extracts the point cloud data from the historical point cloud database based on the sampling step size. Previous point cloud data collection. Referring to FIG. 8, the system detects that there is random noise in the k+1th frame to the nth frame in the historical point cloud library, determines the sampling step size as n frames, ignores the history frame with noise, and extracts before the noise.
  • the history frame (such as the first frame to the kth frame in the figure) is used as the point cloud data set.
  • only one historical frame that is not affected by the random noise can be extracted based on the sampling step size to partially achieve the effect of filtering out noise of different frequencies in the embodiment of the present invention.
  • the image captured during the ridge will be blurred, if the two frames before and after the ridge are extracted, because the two frames The images are all clear, so the results of point cloud registration for the steps described below will also be accurate. In this way, the error caused by the random noise when the ridge is over, will not spread to the measurement after the ridge.
  • the system detects that the order of the current frame point cloud data conforms to a preset order, extracting k frames preceding the current frame point cloud data as a point cloud data set. If the system detects that the order of the current frame point cloud data does not conform to the preset order, extracting the previous frame of the current frame point cloud data as the point cloud data set. Referring to FIG. 9, if the order of the current frame is the 5th frame, the 10th frame, the 15th frame, etc., the first 4 frames of historical frame point cloud data of the current frame point cloud data are extracted as the point cloud data set, and If the current frame does not meet the above periodic order, the historical frame point cloud data of the previous frame of the current data frame is extracted as the point cloud data set.
  • step S204 Perform point cloud registration on each of the current frame point cloud data and the point cloud data set in the point cloud data set to obtain multiple state estimation values of the current frame point cloud data, and each state.
  • the weight value corresponding to the estimated value For details, refer to the description of step S103 in the embodiment of FIG. 3, and details are not described herein again.
  • the state estimation value includes an expected value and a weight value; wherein the weight value is determined by a coincidence degree value of the current frame point cloud data and the historical frame point cloud data; the weight value is linear And superimposing the N state estimation values to obtain a state estimation result of the current frame point cloud data, including: performing the expected value of the N state estimation values based on the corresponding weight value The weighted superposition obtains a state estimation result of the current frame point cloud data.
  • the mathematical form of the state estimate may be a probability distribution estimate (eg, a normal distribution), a variance, a covariance, a covariance matrix, a non-probability distribution estimate, and the like.
  • the car runs through three positions A, B, and C, and the point cloud data collected at each point are A frame, B frame, and C frame, respectively.
  • the state estimation value of the obtained A frame is represented by a mathematical expectation a
  • the weight value is represented by a normal distribution of the variance ⁇ 2 .
  • the mathematical expectation of the B frame relative to the A frame whose position point advance distance is ⁇ x 1 and the normal deviation of the variance ⁇ 1 2 is calculated, and the state estimation value of the B frame is mathematical.
  • b (a + ⁇ x 1 ) and the weight value is characterized by a normal distribution of variance ( ⁇ 2 + ⁇ 1 2 ).
  • the technical solution of the embodiment of the present invention is applied, and the C-frame and the A-frame and the B-frame respectively perform point cloud registration, and the mathematical expectation of calculating the advancement distance of the C frame relative to the B frame position is ⁇ x 2 and the variance is ⁇ 2 .
  • the obtained two state evaluation values c 1 and c 2 of the C point and the corresponding weight values are weighted and superimposed, and the superposition process can be calculated by using the formula of the mean square error, thereby obtaining the total state estimation of the C point.
  • the object of the SLAM system triggers the current frame point cloud data and the plurality of historical frame point cloud data to perform point cloud registration through a preset policy during normal driving, and obtains several related currents.
  • the state estimation value and the weight value of the position or posture are weighted and superimposed, and the irregular noise is eliminated by the algorithm in the superimposition process.
  • the accuracy improvement is real-time (and the accuracy of the conventional loopback motion)
  • the improvement is not real-time), which can further improve the accuracy of drawing and positioning in subsequent applications, which is conducive to the practical use of automatic driving products and enhance the user experience.
  • Fig. 11a shows the results obtained by constructing the trajectory of the vehicle under test on the uneven road surface using the conventional SLAM scheme, and the result is in two.
  • the dimensional coordinate graph is presented, and the gray trajectory in the figure is the running trajectory of the obtained automobile. It can be seen that due to the serious influence of random noise, the running track is staggered and the lines are not continuous, and the result of the drawing is greatly errored.
  • Figure 11b shows the results obtained by plotting the trajectory of the vehicle under test on an uneven road surface using the SLAM scheme provided by the embodiment of the present invention under equivalent test conditions. It can be seen that the two-dimensional coordinate graph shows that the running track line is smooth and flat, and the lines are continuous. That is to say, the embodiment of the present invention can greatly eliminate the influence of noise on subsequent frames, and the error of the construction result is small.
  • Figure 12a shows the results of mapping the trajectory of the vehicle under test on a rough road using a conventional SLAM scheme.
  • the artificially adjusted parameters are used to remove the Kalman filter image frame with noise.
  • the obtained driving track has continuous lines, but the error between the starting point and the ending point in the illustrated result is about 250 meters.
  • the result of the mapping is relatively large, and the reliability of the result is low, which is not conducive to the actual product application.
  • Figure 12b shows the results obtained by constructing the trajectory of the vehicle under test on the uneven road surface using the SLAM scheme provided by the embodiment of the present invention under the same test conditions.
  • the graph shows that the running track line is smooth and flat, the lines are continuous, and the error of the starting point and the ending point in the illustrated result is about 70 meters. That is, the embodiment of the present invention can greatly reduce the error of the drawing positioning compared with the conventional scheme. The accuracy of the car position evaluation is greatly improved, and the result is highly reliable.
  • random noise means that the system may encounter various frequencies or intensity noise, this means that the manual adjustment method is only suitable for the characteristic noise of a specific scene, and cannot use a set of universal parameters. All noise can be filtered out, and the effect of the system will be worse after the scene is changed.
  • the embodiment of the present invention can automatically filter for various irregular noises, and is applicable to various application scenarios, and the actual product is more practical.
  • FIG. 13 is a structural block diagram of an implementation manner of a state-aware SLAM terminal 300 according to an embodiment of the present invention.
  • the SLAM terminal 300 can include a chip 310, a memory 315 (one or more computer readable storage media), and a peripheral system 317. These components can communicate over one or more communication buses 314.
  • the peripheral system 317 is mainly used to implement the interaction function between the SLAM terminal 300 and the user/external environment.
  • the peripheral system 317 may include: a touch screen controller 318, a 3D camera controller 319, a lidar controller 320, and sensor management.
  • Several components in module 321 Each controller may be coupled to a corresponding peripheral device such as a touch screen 323, a 3D camera 324, a laser radar 325, and a sensor 326, etc., wherein the 3D camera controller 319, the lidar controller 320, and the sensor 326 are all sensor.
  • the sensor can be one or more of a speedometer, an accelerometer, an odometer GPS. It should be noted that the peripheral system 317 may also include other I/O peripherals.
  • the chip 310 can be integrated to include one or more processors 311, a clock module 312, and possibly a power management module 313.
  • the clock module 312 integrated in the chip 310 is primarily used to generate the clocks required for data transfer and timing control for the processor 311.
  • the power management module 313 integrated in the baseband chip 310 is primarily used to provide a stable, high accuracy voltage to the processor 311 and peripheral systems.
  • Memory 315 is coupled to processor 311 for storing data (such as a historical point cloud), various software programs, and/or sets of program instructions.
  • memory 315 can include high speed random access memory, and can also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid state storage devices.
  • the memory 315 can also store one or more applications. As shown in the figure, these applications may include: a map application, an image management application, and the like.
  • the SLAM terminal 300 is only an example provided by an embodiment of the present invention, and that the SLAM terminal 300 may have more or fewer components than the illustrated components, may combine two or more components, or may have Different configurations of components are implemented.
  • the 3D camera 324 or the laser radar 325 or the sensor 326 can be used to collect current frame point cloud data; the memory 312 is used to store a historical point cloud library; the processor 311 can be used to call program instructions in the memory. Perform the following program steps:
  • the state estimation value includes an expected value and a weight value; wherein the weight value is determined by a coincidence degree value of the current frame point cloud data and the historical frame point cloud data; the weight value is linear
  • the processor 311 is configured to perform superimposing the plurality of state estimation values to obtain a state estimation result of the current frame point cloud data, where the processor 311 is configured to perform the N state estimation values.
  • Superimposing, obtaining a state estimation result of the current frame point cloud data comprising: performing weighted superposition on the expected value of the N state estimation values based on the corresponding weight value, to obtain a state of the current frame point cloud data Estimated results.
  • the method further includes: detecting random noise; the historical point cloud library The N historical frame point cloud data is extracted, and the processor 311 is configured to: when the random noise is detected, trigger the extracting the N historical frame point cloud data from the historical point cloud library.
  • the method further includes: the processor 311 is configured to perform the detecting the current frame point cloud data.
  • the acquiring sequence is in a preset order; the processor 311 is configured to perform the extracting the N historical frame point cloud data from the historical point cloud library, where the processor 311 is configured to perform the detecting the current frame point cloud data.
  • the triggering extracts N historical frame point cloud data from the historical point cloud library.
  • the processor 311 is configured to perform extracting a point cloud data set before the current frame point cloud data from the historical point cloud database, where the processor 311 is configured to perform determining the sampling step according to the random noise. Long; extracting a point cloud data set from the historical point cloud database before the current frame point cloud data based on the sampling step size.
  • the mathematical form of the state estimation value includes at least one of a probability distribution estimation, a variance, a covariance, a covariance matrix, and a non-probability distribution estimation.
  • the embodiment of the present invention provides another apparatus 400 for state awareness.
  • the apparatus 400 includes: an acquisition module 401, an extraction module 402, a registration module 403, and a superposition module 404.
  • the specific description is as follows:
  • the collecting module 401 is configured to collect current frame point cloud data
  • the extraction module 402 is configured to extract N historical frame point cloud data from the historical point cloud library, where the collection time of the N historical frames is before the acquisition time of the current frame, where N is greater than or equal to 1. Positive integer
  • the registration module 403 is configured to perform point cloud registration on the current frame point cloud data and the N historical frame point cloud data respectively, to obtain a plurality of state estimation values of the current frame point cloud data;
  • the superimposing module 404 is configured to superimpose the N state estimation values to obtain a state estimation result of the current frame point cloud data.
  • the state estimation value includes an expected value and a weight value; wherein the weight value is determined by a coincidence degree value of the current frame point cloud data and the historical frame point cloud data; the weight value is linear Distribution to indicate;
  • the superimposing module 404 is configured to superimpose the plurality of state estimation values to obtain a state estimation result of the current frame point cloud data, including: the superposition module 404 is configured to use the N state estimation values The expected value is weighted and superimposed based on the corresponding weight value to obtain a state estimation result of the current frame point cloud data.
  • the device further includes a detecting module 405, and the detecting module 405 is configured to detect random noise;
  • the extracting module 402 is configured to extract N historical frame point cloud data from the historical point cloud library, including:
  • the extraction module 402 is triggered to extract N historical frame point cloud data from the historical point cloud library.
  • the detecting module 405 is configured to detect whether the collection order of the current frame point cloud data conforms to a preset order
  • the extracting module 402 is configured to extract N historical frame point cloud data from the historical point cloud database, including: when the detecting module 405 detects that the current frame point cloud data collection order meets a preset order, The extracting module 402 is triggered to extract N historical frame point cloud data from the historical point cloud library.
  • the extracting module 405 extracts N historical frame point cloud data from the historical point cloud database, including: the extracting module 402 is configured to determine a sampling step according to the random noise; The sampling step size extracts N historical frame point cloud data from the historical point cloud library.
  • the mathematical form of the state estimation value includes at least one of a probability distribution estimation, a variance, a covariance, a covariance matrix, and a non-probability distribution estimation.
  • the computer program product comprises one or more computer instructions which, when loaded and executed on a computer, produce, in whole or in part, a process or function according to an embodiment of the invention.
  • the computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable device.
  • the computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be from a network site, computer, server or data center Transmission to another network site, computer, server, or data center via wired (eg, coaxial cable, fiber optic, digital subscriber line) or wireless (eg, infrared, microwave, etc.).
  • the computer readable storage medium can be any available media that can be accessed by a computer, or can be a data storage device such as a server, data center, or the like that includes one or more available media.
  • the usable medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape, etc.), an optical medium (such as a DVD, etc.), or a semiconductor medium (such as a solid state hard disk) or the like.
  • a magnetic medium such as a floppy disk, a hard disk, a magnetic tape, etc.
  • an optical medium such as a DVD, etc.
  • a semiconductor medium such as a solid state hard disk

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Abstract

La présente invention concerne un procédé de détection d'état et un appareil associé. Le procédé de détection d'état consiste à : acquérir des données de nuage de points d'une image actuelle (S101); extraire des données de nuage de points de N images historiques à partir d'une bibliothèque de nuages de points historiques (S102), l'instant d'acquisition de chacune des N images historiques étant antérieur à l'instant d'acquisition de l'image actuelle, N étant un entier positif supérieur ou égal à 1; effectuer un recalage de nuages de points sur les données de nuage de points de l'image actuelle et les données de nuage de points des N images historiques afin d'obtenir N valeurs d'estimation d'état des données de nuage de points de l'image actuelle (S103); et superposer les N valeurs d'estimation d'état pour obtenir un résultat d'estimation d'état des données de nuage de points de l'image actuelle (S104). Le procédé et l'appareil éliminent par filtrage un bruit irrégulier sans nécessiter un mouvement en boucle, de façon à obtenir un résultat de détection d'état plus précis, et à améliorer la précision lors de la localisation et de la cartographie d'un système de localisation et de cartographie simultanées (SLAM).
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CN118962646A (zh) * 2024-10-17 2024-11-15 北京理工大学前沿技术研究院 一种基于强度和位置信息的回环检测方法和系统

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