IMU-Assisted Target-Free Extrinsic Calibration of Heterogeneous Lidars Based on Continuous-Time Optimization

Abstract

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Data fusion of heterogeneous LiDAR systems has gained significant attention due to its potential for providing wide-range sensing and high-density measurements for robots. However, existing LiDAR calibration methods primarily focus on homogeneous LiDAR systems and yield suboptimal outcomes when applied to heterogeneous setups. To this end, this paper proposes an IMU-Assisted Heterogeneous LiDAR extrinsics Calibration method, namely IA-HeLiC, which is a target-free method based on continuous-time optimization. Specifically, IA-HeLiC utilizes two types of errors, namely geometric constraint error and motion constraint error, and minimizes them within a B-spline-based continuous-time framework to achieve accurate extrinsic calibration. Using a parameter loopback mechanism, this optimization process is performed iteratively to further improve calibration accuracy. IA-HeLiC’s performance is corroborated through experiments using a ground-truth-known handheld device, by which multiple data sequences were collected in diverse real-world scenes. To make our results reproducible, the source code and the collected dataset have been released at https://cslinzhang.github.io/IA-HeLiC.