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Publication type: Article in Proceedings
Author: Joachim Clemens, Constantin Wellhausen, Tom Koller, Udo Frese, Kerstin Schill
Title: Kalman Filter with Moving Reference for Jump-Free, Multi-Sensor Odometry with Application in Autonomous Driving
Book / Collection title: 2020 IEEE 23rd International Conference on Information Fusion (FUSION)
Volume: 23
Series: IEEE
Year published: 2020
Publisher: IEEE Computer Society
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.
ISBN: 978-0-578-64709-8
Keywords: Quaternions Kalman filters Sensors Current measurement Uncertainty Manifolds Noise measurement
Status: Reviewed
Last updated: 15. 09. 2020

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