Lidar SLAM Comparison in a Featureless Tunnel Environment

Abstract

Simultaneous Localization and Mapping (SLAM) algorithm consists a vital part of decision-making process of autonomous robot platforms. Many lidar-based SLAM methods have been proposed for indoor and urban environments. However, a few studies are reported in a featureless tunnel environment. In this paper we consider recent lidar SLAM frameworks and test their performance in a tunnel environment. Our dataset is collected by a four-wheeled ground vehicle that is equipped with a lidar sensor used for mapping and feature detection and an IMU sensor used for odometry tracking information. The performance of seven different lidar SLAM algorithms is tested and as a result, in corridor environment LIO-SAM and SC-LIO-SAM frameworks show the lowest trajectory and point cloud error, respectively. On the other hand, LIO-SAM and FAST-LIO2 displays the best trajectory accuracy in the tunnel environment with addition of artificial landmarks and without them, respectively. The results obtained during the performance of seven different lidar SLAM algorithms can contribute to the development of a SLAM framework in a featureless tunnel environment.