Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving

• URL: http://arxiv.org/abs/2311.01823v1
• Date: Fri, 3 Nov 2023 10:24:20 GMT
• Title: Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving
• Authors: Florian Sauerbeck, Dominik Kulmer, Markus Pielmeier, Maximilian Leitenstern, Christoph Wei{\ss}, Johannes Betz
• Abstract summary: We present a novel sensor fusion-based pipeline for offline mapping and online localization based on LiDAR sensors. We introduce an approach to generate semantic maps for driving tasks such as path planning. We show that our pipeline outperforms state-of-the-art approaches for a given research vehicle and real-world autonomous driving application.
• Score: 1.5728609542259502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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