Automatic Multi-LiDAR Calibration and Refinement Method

TL;DR: This research aims to develop a robust, fast, automatic, and accurate calibration strategy for multiple LiDAR systems, and proposes the octree-based refinement, an optimization method that considers sensor noise and prioritization.

Abstract:  Sensor-based environmental perception is a crucial component of autonomous driving systems. To perceive the surrounding environment better, an intelligent system would utilize multiple LiDARs (3D Light Detection and Ranging). The accuracy of the perception largely depends on the quality of the sensor calibration. This research aims to develop a robust, fast, automatic, and accurate calibration strategy for multiple LiDAR systems. Our proposed multi-LiDAR calibration method consists of two stages: rough and refinement calibration. In the first stage, sensors are roughly calibrated from an arbitrary initial position using a deep neural network that does not rely on prior information or constraints on the initial sensor pose. In the second stage, we propose the octree-based refinement, an optimization method that considers sensor noise and prioritization. Our strategy is robust, fast, and not restricted to any environment. Additionally, we collected two datasets consisting of both real-world and simulated scenarios. Our experimental results from both datasets demonstrate the reliability and accuracy of our method. All the related datasets and codes are open-sourced on the GitHub website at https://github.com/OpenCalib/LiDAR2LiDAR.