Extrinsic Calibration of Multiple LiDARs for a Mobile Robot Based on Floor Plane and Object Segmentation

The utilization of mobile robots equipped with multiple light detection and ranging (LiDAR) sensors, capable of perceiving their surroundings, is on the rise due to the miniaturization and cost reduction of LiDAR technology. This paper introduces a target-less extrinsic calibration method for multiple LiDARs with non-overlapping fields of view (FoV). The proposed method leverages accumulated point clouds of the floor plane and objects obtained during robot motion. It enables accurate calibration, even in challenging configurations where LiDARs are directed towards the floor plane, which can introduce biased feature values. Additionally, the method incorporates a noise removal module that takes into account the scanning pattern to address bleeding points, which are significant sources of error in point cloud alignment when using high-density LiDARs. Evaluations conducted through simulation demonstrate that the proposed method achieves higher accuracy in extrinsic calibration with two and four LiDARs compared to conventional methods, regardless of the type of objects. Furthermore, experiments conducted using a real mobile robot validate the effectiveness of our proposed noise removal module in precisely eliminating noise compared to conventional methods. The estimated extrinsic parameters successfully contribute to the creation of consistent 3D maps.