Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous
  Driving
Florian Sauerbeck Dominik Kulmer Markus Pielmeier Maximilian Leitenstern Christoph Weiß Johannes Betz
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.