Leveraging Multimodal Sensing and Topometric Mapping for Human-Like Autonomous Navigation in Complex Environments
Kosmas Tsiakas, Dimitrios Alexiou, Dimitrios Giakoumis, Antonios Gasteratos, Dimitrios Tzovaras
Abstract
Autonomous vehicle navigation in complex and unpredictable outdoor environments requires extensive and detailed understanding of the surrounding area and compliance with the traffic rules. In this paper, we attempt to imitate human driver behavior towards autonomous navigation that is suitable for diverse, challenging environments, whether urban, semi-structured or rural-like. Our approach starts with a novel method that we propose for extracting free space area using RGB and LiDAR data, in combination with a rough topometric map for route planning. Local goals are extracted in the final drivable region, and the vehicle draws a local path in the free space that is approximately in line with the overall path via a lattice planner. Our method is evaluated both in the publicly available KITTI urban dataset and a custom-made dataset of a semi-structured environment. In both cases, the results highlight the potential of our approach for further advancements in autonomous navigation and the development of safer and more human-like behaviors in driverless vehicles compared to the existing trajectory prediction state-of-the-art methods that make use of a topometric map.
BibTeX
@inproceedings{iros2023_leveragingmultim,
title = {Leveraging Multimodal Sensing and Topometric Mapping for Human-Like Autonomous Navigation in Complex Environments},
author = {Kosmas Tsiakas and Dimitrios Alexiou and Dimitrios Giakoumis and Antonios Gasteratos and Dimitrios Tzovaras},
booktitle = {IROS 2023},
year = {2023}
}