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Kinematic parameter calibration of a car-like mobile robot to improve odometry accuracy

机译:类似于汽车的移动机器人的运动学参数校准,可提高里程计的准确性

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摘要

Odometry provides fundamental pose estimates for wheeled vehicles. For accurate and reliable pose estimation, systematic and nonsystematic errors of odometry should be reduced. In this paper, we focus on systematic error sources of a car-like mobile robot (CLMR) and we suggest a novel calibration method. Kinematic parameters of the CLMR can be successfully calibrated by only a couple of test driving. After reducing deterministic errors by calibration, odometry accuracy can be further improved by redundant odometry fusion with the extended Kalman filter (EKF). Odometry fusion reduces nonsystematic or stochastic errors. Experimental verifications are carried out using a radio-controlled miniature car.
机译:里程表为轮式车辆提供基本的姿态估计。为了准确可靠地估计姿势,应减少里程表的系统误差和非系统误差。在本文中,我们将重点放在类似汽车的移动机器人(CLMR)的系统误差源上,并提出一种新颖的校准方法。仅通过几次试驾就可以成功校准CLMR的运动学参数。通过校准减少确定性误差后,可通过与扩展卡尔曼滤波器(EKF)进行冗余里程计融合来进一步提高里程计精度。里程表融合减少了非系统性或随机性错误。实验验证是使用无线电遥控微型汽车进行的。

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