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Vehicle Localization Based on Visual Lane Marking and Topological Map Matching

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Abstract

Accurate and reliable localization is crucial to autonomous vehicle navigation and driver assistance systems. This paper presents a novel approach for online vehicle localization in a digital map. Two distinct map matching algorithms are proposed: i) Iterative Closest Point (ICP) based lane level map matching is performed with visual lane tracker and grid map ii) decision-rule based approach is used to perform topological map matching. Results of both the map matching algorithms are fused together with GPS and dead reckoning using Extended Kalman Filter to estimate vehicle's pose relative to the map. The proposed approach has been validated on real life conditions on an equipped vehicle. Detailed analysis of the experimental results show improved localization using the two aforementioned map matching algorithms
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Dates and versions

hal-03043594 , version 1 (01-06-2021)

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Rabbia Asghar, Mario Garzón, Jerome Lussereau, Christian Laugier. Vehicle Localization Based on Visual Lane Marking and Topological Map Matching. ICRA 2020 - IEEE International Conference on Robotics and Automation, May 2020, Paris, France. pp.258-264, ⟨10.1109/ICRA40945.2020.9197543⟩. ⟨hal-03043594⟩
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