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Article Dans Une Revue IEEE Robotics and Automation Letters Année : 2020

Improved Data Association Using Buffered Pose Adjustment for Map-Aided Localization

Résumé

Maps provide an important source of information for autonomous vehicles. They can be used along with cameras and lidars to localize the vehicle. This requires the ability to correctly associate observations to features referenced in the map. The problem is all the more difficult than all observations are not necessarily referenced, and all map features might not be detectable with the embedded sensors. This paper presents an adjustment technique that enables to increase the number of associations that can be made while limiting the chance of obtaining wrong associations. This is achieved by matching observations in batches in a buffer and matching them regularly. Periodically, the observation buffer is used to adjust the trajectory used to match observations. This is done without making any assumption on the association between observations and map features through a likelihood maximization process. The adjusted trajectory then provides the best associations that are used for real-time localization. The method was tested with data recorded on public roads using an experimental vehicle. The results show that thanks to the trajectory adjustment step and the use of an observation buffer, the number of associations that can be made is increased. This also results in greater localization accuracy and consistency with an average error of 0.7 meters at 50 Hz using road markings and traffic signs.
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Dates et versions

hal-02929110 , version 1 (03-09-2020)

Identifiants

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Anthony Welte, Philippe Xu, Philippe Bonnifait, Clément Zinoune. Improved Data Association Using Buffered Pose Adjustment for Map-Aided Localization. IEEE Robotics and Automation Letters, 2020, 5 (4), pp.6334-6341. ⟨10.1109/LRA.2020.3013856⟩. ⟨hal-02929110⟩
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