Automatic Multiple LiDAR Calibration Based on the Plane Features of Structured Environments

The multiple light detection and ranging (LiDAR) system has been attached to numerous autonomous vehicles to reduce blind spot risks and increase measurement resolution. It is also important to accurately aligning multi-LiDAR data because misaligned sensor data can affect the localization and perception algorithms of LiDAR sensors. However, precise extrinsic parameter estimation is a challenging research area because the feature points in LiDAR data cannot be precisely aligned owing to the LiDAR resolution. In this paper, we proposed a targetless multi-LiDAR calibration method without any initial information. In the proposed approach, a random sample consensus (RANSAC)-based plane fitting technique was utilized for plane extraction, and cross-feature points were extracted from the polar view transform of segmented planes. To resolve the discontinuity problem of optimization, a nonlinear optimization method was utilized, and the experiments were performed within a structured environment. Overall, the experimental results showed that the proposed method yields comparable results with state-of-the-art methods.