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

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 removi...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2023-02, Vol.8 (2), p.1029-1036
Main Authors: Vizzo, Ignacio, Guadagnino, Tiziano, Mersch, Benedikt, Wiesmann, Louis, Behley, Jens, Stachniss, Cyrill
Format: Article
Language:English
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
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3236571