Abstract
This paper discusses the current methods for vehicle self-localization and compares previous findings to the use for urban public traffic vehicles. In specific, requirements for autonomous buses approaching a bus stop are defined. An autonomous system capable of reliable vehicle self-localization running in real-time in a city scenario shall be developed in a future work based on this paper. The comparison of filter-based estimation and graph-based optimization techniques shows that the latter suits the the automated approach to a bus stop in an urban environment the best. Based on these findings, a concept for self-localization of public transport vehicles equipped with a variety of imaging sensors with the help of a digital high definition map is presented. A current method is shown and a concept of improving the localization by inferring semantic information into landmark detection by low-level data fusion is provided. Validation and verification of the proposed fusion approach have to be carried out in the future, but a validation scenario is presented in this work.
Original language | English |
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Pages (from-to) | 13-20 |
Number of pages | 8 |
Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
Volume | 42 |
Issue number | 2/W16 |
DOIs | |
State | Published - 17 Sep 2019 |
Event | 2019 Joint ISPRS Conference on Photogrammetric Image Analysis and Munich Remote Sensing Symposium, PIA 2019+MRSS 2019 - Munich, Germany Duration: 18 Sep 2019 → 20 Sep 2019 |
Keywords
- Automated Driving
- Graph Optimization
- High Definition Map
- Low-level Data Fusion
- SLAM
- Self-Localization