Fast calibration of rotating and swivelling 3-D laser scanners exploiting measurement redundancies

Abstract

New sensor systems, efficient planning algorithms and increased computational power have led to a growing interest in high-resolution 3-D data for challenging applications like mobile manipulation, 3-D mapping and object recognition. 3-D laser scanners built using a rotating or swivelling 2-D laser scanner are a widespread technology for obtaining 3-D point clouds, but convenient calibration methods are needed to increase the sensor precision and loosen the requirements on mechanical tolerances. We present an approach for the automatic self-calibration of such scanners, using only a single scan of a targetless environment recorded over a complete 360° rotation of the external motor that controls the motion of the 2-D sensor. By exploiting the intrinsic redundancies of the recorded point clouds and decimating the clouds using a voxel grid filter, we are able to compute and optimize a point cloud quality measure very quickly and without relying on explicit calibration targets. Our results on simulated and real data show the effectiveness of our approach by creating high-quality 3-D point clouds.