In spite of rapid advancements of the autonomous car technology in recent years, the self-localization of a commercial autonomous driving vehicles is still an open problem. This paper reports on the problems of LiDAR and map based localization in urban environments for autonomous vehicles. In recent years many researches have been done in the area of precise map generation. These researches and availability of various data sources together increased the accuracy of the generated maps. Usually, size of a map in the format of point cloud is quite big. On the other hand, features in a simple 2D map is not enough for an accurate localization because it cannot represent surfaces of one building in different heights. To address these problems, we proposed a LiDAR based localization technique utilizes multi-layer 2D map which encapsulates the feature from different layers of a building in 2D map. Experimental results show that the mean error of our proposed self-localization technique is less than 24cm compared with the conventional point cloud map matching, while the size of map is reduced significantly.
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University of Tokyo, IIS Live Poster by Ehsan Javanmardi
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GCL MUSCat Project Meeting with CEO of U’s factory for Hongo campus 3D map reconstruction
#GCL
#MUSCaT
#U's factory
#UTokyo
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#3dmapping
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MUSCaT Poster for GCL Visit from MEXT and JSPS
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The Bonds of Brotherhood Accelerate Autonomous Driving and Bridge the Gap between Iran and Japan
Ehsan and Mahdi JavanmardiDoctoral degree students, Graduate School of Information Science and Technology (IST) Area of research: Information and communication engineering Country/Region of Origin: Iran
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