Localization Accuracy of Autonomous Mobile Robots: A Comprehensive Evaluation of KISS-ICP Odometry

Localization Accuracy of Autonomous Mobile Robots: A Comprehensive Evaluation of KISS-ICP Odometry

Stať ve sborníku mimo WoS a Scopus

This paper emphasizes the importance of accurate localization for the appropriate behavior of autonomous mobile robots. In particular, it provides a rigorous evaluation of the accuracy of the KISS-ICP algorithm, a lightweight lidar-based pose estimation algorithm known for its simplicity with minimal setup parameters. The algorithm works only with lidar data; unlike other more sophisticated SLAM algorithms, it does not use IMUs. The algorithm is shown to work under a variety of conditions and sensors. To comprehensively evaluate how the algorithm performs under varying conditions, several sensors were used in the experiments. The evaluation included a series of 3D lidars, namely Ouster OS0, OS1, and OS2. These lidars are characterized by different field-of-view settings and operating modes with different point per row. In order to facilitate this evaluation, a data set with extensive robot traversals over a distance of more than 9 km in two different environments, each equipped with different sensor types, was prepared.

This paper emphasizes the importance of accurate localization for the appropriate behavior of autonomous mobile robots. In particular, it provides a rigorous evaluation of the accuracy of the KISS-ICP algorithm, a lightweight lidar-based pose estimation algorithm known for its simplicity with minimal setup parameters. The algorithm works only with lidar data; unlike other more sophisticated SLAM algorithms, it does not use IMUs. The algorithm is shown to work under a variety of conditions and sensors. To comprehensively evaluate how the algorithm performs under varying conditions, several sensors were used in the experiments. The evaluation included a series of 3D lidars, namely Ouster OS0, OS1, and OS2. These lidars are characterized by different field-of-view settings and operating modes with different point per row. In order to facilitate this evaluation, a data set with extensive robot traversals over a distance of more than 9 km in two different environments, each equipped with different sensor types, was prepared.

Localization, SLAM, Outdoor navigation, ROS

Localization, SLAM, Outdoor navigation, ROS

CIHLÁŘ, M.

23.04.2024

Brno University of Technology, Faculty of Electronic Engineeringy and Communication

Brno

319

326

8

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf