Two-Stage Extrinsic Calibration of a Static Line-Scanning Lidar with a Rotary Platform

2026-07-20

AI Summary

This study addresses the extrinsic calibration between the rotation axis of a turntable and a static line-scanning LiDAR to ensure high-fidelity 3D point cloud reconstruction. The work proposes a two-stage, fully automatic calibration method that uniquely integrates static and dynamic observation strategies, enabling robust extrinsic parameter estimation by coherently fusing complementary information from both modalities. Built upon an optimization-based framework, the approach effectively mitigates convergence challenges across diverse initial conditions. Validation on real-world data collected using an FMCW LiDAR and a custom-built rotating platform demonstrates the method’s reliability and accuracy. Experimental results show consistent convergence under various initialization settings and yield reconstructed point clouds with excellent geometric fidelity.

Abstract

A line-scanning lidar yields range and azimuth values in a fixed plane. To perceive surrounding objects in 3D, there must be relative motion between the lidar plane and the object. Thus, using a rotating base-platform is promising for industrial applications where objects need to be scanned or inspected precisely, and is the main focus of this work. In the rotary platform setup, a 3D point cloud of an object can be constructed if the axis of rotation and the precise motion about that axis are known. However, this setup gives rise to the following problem: how can the axis of rotation of the platform be accurately identified with respect to the lidar coordinate system? It is referred to as the calibration problem in the robotics community. Any inaccuracy in this transformation directly affects the quality of the reconstructed point cloud, leading to misrepresentation of the object of interest. In this work, we explore automated approaches to statically and dynamically estimate the transformation of a rotary platform's axis of rotation with respect to a static line-scanning lidar. The proposed algorithms have been validated on real-world datasets obtained from a custom made rotary platform and an FMCW lidar, and their convergence characteristics are studied for various initial conditions.

Problem

• extrinsic calibration
• line-scanning lidar
• rotary platform
• axis of rotation
• point cloud reconstruction

Innovation

• two-stage calibration
• line-scanning lidar
• rotary platform
• extrinsic calibration
• 3D point cloud reconstruction