A high definition (HD) map, which provides prior knowledge to autonomous driving tasks, has been attracted in recent years. An HD map-based self-localization is a crucial technology for autonomous driving, but its accuracy is greatly affected by occlusions caused by dynamic obstacles in real environments. This paper focuses on clarifying the need for HD maps for stable self-localization in highly dynamic environments, especially in an urban canyon. By comparing the effects of occlusion with synthetically generated obstacles in a real environment, we show significant accuracy degradations in a general self-localization method due to obstacles in Shinjuku, Tokyo, Japan. In addition, we reveal that pole-like objects can be vital elements of an HD map to stabilize self-localization accuracy even with many obstacles by evaluating various patterns of high occlusion cases.
Evaluation of High Definition Map-Based Self-Localization Against Occlusions in Urban Area
2021-07-11
5184758 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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