KISS-ICP: In Defense of Point-to-Point ICP Simple, Accurate, and Robust Registration If Done the Right Way

• Published in: IEEE robotics and automation letters Vol. 8; no. 2; pp. 1 - 8
• Main Authors: Vizzo, Ignacio, Guadagnino, Tiziano, Mersch, Benedikt, Wiesmann, Louis, Behley, Jens, Stachniss, Cyrill
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automobiles; Estimation; Laser radar; Lidar; Localization; Mapping; Motion compensation; Motion simulation; Odometers; Optimization; Parameters; Platforms; Point cloud compression; Pose estimation; Robotics; Robustness; Sensor systems; Sensors; SLAM; Three dimensional models; Three-dimensional displays
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2023.3236571

Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removing a majority of parts and focusing on the core elements, we obtain a surprisingly effective system that is simple to realize and can operate under various environmental conditions using different LiDAR sensors. Our odometry estimation approach relies on point-to-point ICP combined with adaptive thresholding for correspondence matching, a robust kernel, a simple but widely applicable motion compensation approach, and a point cloud subsampling strategy. This yields a system with only a few parameters that in most cases do not even have to be tuned to a specific LiDAR sensor. Our system performs on par with state-of-the-art methods under various operating conditions using different platforms using the same parameters: automotive platforms, UAV-based operation, vehicles like segways, or handheld LiDARs. We do not require integrating IMU data and solely rely on 3D point clouds obtained from a wide range of 3D LiDAR sensors, thus, enabling a broad spectrum of different applications and operating conditions. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.