Publication
Improving map re-localization with deep 'movable' objects segmentation on 3D LiDAR point clouds
Conference Article
Conference
IEEE Intelligent Transportation Systems Conference (ITSC)
Edition
2019
Pages
942-949
Doc link
https://doi.org/10.1109/ITSC.2019.8917390
File
Authors
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Vaquero Gomez, Victor
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Fischer, Kai
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Moreno Noguer, Francesc
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Sanfeliu Cortés, Alberto
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Milz, Stefan
Abstract
Localization and Mapping is an essential compo-nent to enable Autonomous Vehicles navigation, and requiresan accuracy exceeding that of commercial GPS-based systems.Current odometry and mapping algorithms are able to providethis accurate information. However, the lack of robustness ofthese algorithms against dynamic obstacles and environmentalchanges, even for short time periods, forces the generationof new maps on every session without taking advantage ofpreviously obtained ones. In this paper we propose the useof a deep learning architecture to segmentmovableobjectsfrom 3D LiDAR point clouds in order to obtain longer-lasting3D maps. This will in turn allow for better, faster and moreaccurate re-localization and trajectoy estimation on subsequentdays. We show the effectiveness of our approach in a verydynamic and cluttered scenario, a supermarket parking lot.For that, we record several sequences on different days andcompare localization errors with and without ourmovableobjects segmentation method. Results show that we are able toaccurately re-locate over a filtered map, consistently reducingtrajectory errors between an average of35.1% with respectto a non-filtered map version and of47.9% with respect to astandalone map created on the current session.
Categories
computer vision, object detection, object recognition.
Author keywords
LiDAR, Vehicle Detection, Deep Learning
Scientific reference
V. Vaquero, K. Fischer, F. Moreno-Noguer, A. Sanfeliu and S. Milz. Improving map re-localization with deep 'movable' objects segmentation on 3D LiDAR point clouds, 2019 IEEE Intelligent Transportation Systems Conference, 2019, Auckland, New Zeland, pp. 942-949.
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