Autonomous vehicles require accurate and robust localization and mapping algorithms to navigate safely and reliably in urban environments. We present a novel sensor fusion-based pipeline for offline mapping and online localization based on LiDAR sensors. The proposed approach leverages four LiDAR sensors. Mapping and localization algorithms are based on the KISS-ICP, enabling real-time performance and high accuracy. We introduce an approach to generate semantic maps for driving tasks such as path planning. The presented pipeline is integrated into the ROS 2 based Autoware software stack, providing a robust and flexible environment for autonomous driving applications. We show that our pipeline outperforms state-of-the-art approaches for a given research vehicle and real-world autonomous driving application.
@article{arxiv.2311.01823,
title = {Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving},
author = {Florian Sauerbeck and Dominik Kulmer and Markus Pielmeier and Maximilian Leitenstern and Christoph Weiß and Johannes Betz},
journal= {arXiv preprint arXiv:2311.01823},
year = {2023}
}
Comments
Accepted and presented at IEEE Sensors Conference 2023