Accurate extrinsic calibration among multiple heterogeneous Light Detection and Ranging (LiDAR) sensors is essential for autonomous vehicle perception systems, yet remains challenging in distributed topologies where overlap exists only between adjacent sensor pairs. Existing methods often assume a central LiDAR with direct field-of-view overlap to all others and suffer from error accumulation in sequential pairwise registration. This paper presents a targetless, motionless multi-LiDAR extrinsic calibration framework that is topology-agnostic and resolves error accumulation through global optimization. The method integrates (1) Random Sample Consensus (RANSAC)-based planar patch extraction with a dual-criterion normal-guided matching strategy, (2) robust coarse alignment via TEASER++, and (3) pose graph optimization with analytically derived edge weights from Generalized Iterative Closest Point (GICP) covariance matrices. The use of structural planar primitives rather than local point descriptors overcomes density-dependent matching failures inherent to heterogeneous sensor pairs, while global pose graph optimization eliminates the cumulative error propagation of sequential pairwise approaches. Validation is performed on three distinct real-world configurations: a six-LiDAR autonomous port truck (ring topology), the four-LiDAR EDGAR research vehicle (distributed topology), and a three-LiDAR benchmark from the OpenCalib toolbox. The proposed method consistently outperforms state-of-the-art baselines, achieving 0.021 m translation Root Mean Square Error (RMSE) and 0.36° rotation RMSE on the port dataset, with full calibration completed in under 2 s on CPU—enabling rapid in-situ recalibration without requiring dedicated facilities or vehicle motion.
Ren et al. (Fri,) studied this question.