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Wheel odometry-based car localization and tracking on vectorial map

Abstract : In this paper, we present a car self-localization approach based on free inputs. We propose to use wheel speeds, which is available on most car through the CAN bus, and community developed road maps. A particle filter framework is used to achieve self-localization on a graph-based representation of a road map. Our results suggests that self-localization and tracking are feasible with these two inputs at a really low computational cost. Car self-localization is achieved with an averaged 5 m accuracy within a 100 km drivable road map on a 12 km sequence.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02289902
Contributor : Xavier Savatier <>
Submitted on : Tuesday, September 17, 2019 - 11:13:15 AM
Last modification on : Sunday, October 25, 2020 - 6:31:23 AM

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P. Merriaux, Y. Dupuis, P. Vasseur, Xavier Savatier. Wheel odometry-based car localization and tracking on vectorial map. 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), Oct 2014, Qingdao, France. pp.1890-1891, ⟨10.1109/ITSC.2014.6957971⟩. ⟨hal-02289902⟩

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