Improving map re-localization with deep 'movable' objects segmentation on 3D LiDAR point clouds
Cita com:
hdl:2117/181140
Tipus de documentText en actes de congrés
Data publicació2019
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
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.
Descripció
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
CitacióVaquero, V. [et al.]. Improving map re-localization with deep 'movable' objects segmentation on 3D LiDAR point clouds. A: International IEEE Conference on Intelligent Transportation Systems. "ITSC 2019 - 22nd International IEEE Conference on Intelligent Transportation Systems". 2019, p. 942-949.
Versió de l'editorhttps://ieeexplore.ieee.org/document/8917390
Col·leccions
- IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Ponències/Comunicacions de congressos [576]
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos [1.500]
- VIS - Visió Artificial i Sistemes Intel·ligents - Ponències/Comunicacions de congressos [292]
- ROBiri - Grup de Percepció i Manipulació Robotitzada de l'IRI - Ponències/Comunicacions de congressos [252]
- Doctorat en Automàtica, Robòtica i Visió - Ponències/Comunicacions de congressos [166]
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
2229-Improving- ... --LiDAR--Point--Clouds.pdf | 2,267Mb | Visualitza/Obre |