FGRSC: Improved Calibration for Spinning LiDAR in Unprepared Scenes

Abstract

Spinning Light Detection And Ranging (LiDAR) is a range scanning device with a larger field of view, which is formed by continuous spinning of a LiDAR with a rotating mechanism. Precise and robust calibration of the relative pose between LiDAR and rotating mechanism is critical for surveying, mapping, localization, etc. However, existing methods generally require ad-hoc tools, well-designed targets, or regular calibrated scenes. For improving calibration in unprepared scenes, we propose a Filtered Grid Random Sample Consensus (FGRSC) calibration approach, constituted by a Filtered Grid (FG) strategy and a Grid Random Sample Consensus (GRSC) method. Based on dividing the whole scene points into small grids when sufficient scanning is received. Firstly, FG is designed to calculate point curvature upon receiving each frame of spinning LiDAR scanning. Then FG normalizes the number of low curvature points to get an approximate probability of containing planes for every grid. Finally, given the probabilities provided by FG, GRSC preselects grids to extract and associate planes to estimate parameters with a weighted optimization. The experimental results derived from indoor and outdoor unprepared scenes show that our FGRSC outperforms the state-of-the-art methods.