Kalman Filter with Moving Reference for Jump-Free, Multi-Sensor Odometry with Application in Autonomous Driving
by , , , ,
Abstract:
Control, tracking, and obstacle detection algorithms for mobile robots, including autonomous cars, rely on a jump- free estimate of the vehicle's pose. While one cannot completely avoid jumps in global solutions like INS/GNSS and SLAM, relative localization (i.e., odometry) does not suffer from this problem. Methods based on graph optimization are popular in that field, but they do not scale very well with high-frequency measurements. Kalman filters (KFs) are able to cope with those measurements, but they face the issue of a continuously growing covariance. This results in instabilities and eventually jumps in the state estimate. We present an approach to handle this problem by periodically moving the reference state forward in time, which is realized using two filters. The equations for implementing this in both the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are derived. The algorithm is evaluated using real-world datasets covering different scenarios of autonomous driving. We show that our method provides a smooth and stable estimate even over long time periods and that it achieves a better localization performance than the standard approach.
Reference:
Kalman Filter with Moving Reference for Jump-Free, Multi-Sensor Odometry with Application in Autonomous Driving (Joachim Clemens, Constantin Wellhausen, Tom Luca Koller, Udo Frese, Kerstin Schill), In 23rd International Conference on Information Fusion (FUSION), IEEE, 2020.
Bibtex Entry:
@inproceedings{clemens2020kfmovref,
	author={Clemens, Joachim and Wellhausen, Constantin and Koller, Tom Luca and Frese, Udo and Schill, Kerstin},
	title = {Kalman Filter with Moving Reference for Jump-Free, Multi-Sensor Odometry with Application in Autonomous Driving},
	booktitle={23rd International Conference on Information Fusion (FUSION)},
	year={2020},
	month=jul,
	publisher={IEEE},
	abstract={Control, tracking, and obstacle detection algorithms
	for mobile robots, including autonomous cars, rely on a jump-
	free estimate of the vehicle's pose. While one cannot completely
	avoid jumps in global solutions like INS/GNSS and SLAM,
	relative localization (i.e., odometry) does not suffer from this
	problem. Methods based on graph optimization are popular in
	that field, but they do not scale very well with high-frequency
	measurements. Kalman filters (KFs) are able to cope with those
	measurements, but they face the issue of a continuously growing
	covariance. This results in instabilities and eventually jumps
	in the state estimate. We present an approach to handle this
	problem by periodically moving the reference state forward
	in time, which is realized using two filters. The equations for
	implementing this in both the extended Kalman filter (EKF) and
	the unscented Kalman filter (UKF) are derived. The algorithm is
	evaluated using real-world datasets covering different scenarios
	of autonomous driving. We show that our method provides a
	smooth and stable estimate even over long time periods and that
	it achieves a better localization performance than the standard
	approach.}
}