[go: up one dir, main page]

Fazekas et al., 2020 - Google Patents

Identification of kinematic vehicle model parameters for localization purposes

Fazekas et al., 2020

Document ID
3768481461513593636
Author
Fazekas M
Gáspár P
Németh B
Publication year
Publication venue
2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

External Links

Snippet

The article proposes a parameter identification algorithm for a kinematic vehicle model from real measurements of on-board sensors. The motivation of the paper is to improve the localization in poor sensor performance cases. For example, when the GNSS signals are …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/10Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration
    • G01C21/12Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/0278Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/28Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30Map- or contour-matching
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0291Fleet control
    • G05D1/0295Fleet control by at least one leading vehicle of the fleet

Similar Documents

Publication Publication Date Title
Helmick et al. Path following using visual odometry for a mars rover in high-slip environments
Boada et al. Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm
Seegmiller et al. Vehicle model identification by integrated prediction error minimization
Zhang et al. A dynamic path search algorithm for tractor automatic navigation
Zaidner et al. A novel data fusion algorithm for low-cost localisation and navigation of autonomous vineyard sprayer robots
EP0570581B1 (en) An improved accuracy sensory system for vehicle navigation
Xia et al. Autonomous vehicles sideslip angle estimation: Single antenna GNSS/IMU fusion with observability analysis
Hasberg et al. Simultaneous localization and mapping for path-constrained motion
Bento et al. Sensor fusion for precise autonomous vehicle navigation in outdoor semi-structured environments
Lenain et al. Adaptive and predictive path tracking control for off-road mobile robots
Saadeddin et al. Optimization of intelligent approach for low-cost INS/GPS navigation system
GB2471276A (en) Terrain sensing apparatus for an autonomous vehicle
Fazekas et al. Vehicle odometry model identification considering dynamic load transfers
Clemens et al. Kalman filter with moving reference for jump-free, multi-sensor odometry with application in autonomous driving
Zhao et al. Vehicle-motion-constraint-based visual-inertial-odometer fusion with online extrinsic calibration
Barbosa et al. Sensor fusion algorithm based on Extended Kalman Filter for estimation of ground vehicle dynamics
Tham et al. Multi-sensor fusion for steerable four-wheeled industrial vehicles
Fazekas et al. Identification of kinematic vehicle model parameters for localization purposes
Azizi et al. Mobile robot position determination using data from gyro and odometry
Betntorp et al. Bayesian sensor fusion for joint vehicle localization and road mapping using onboard sensors
Hidalgo-Carrio et al. Static forces weighted Jacobian motion models for improved Odometry
Krantz et al. Non-uniform dead-reckoning position estimate updates
Fazekas et al. Model based vehicle localization via an iterative parameter estimation
Fazekas et al. Challenges of the application of front-wheel odometry for vehicle localization
Conejo et al. A zonotopic FDI approach with LPV-based EKF in autonomous vehicles