GICP-Based Registration Flow Improvement and Planar Consistency Evaluation for Heterogeneous Multi-LiDAR Systems in Grain Warehousing Robots

Grain intake is a key operation in grain storage that directly affects storage efficiency, operational safety, and grain quality. In grain-entry scenarios, single LiDAR sensors are easily limited by blind spots and occlusions, making multi-LiDAR collaborative perception necessary for reliable three-dimensional environment sensing. However, heterogeneous LiDARs differ in scan lines, point density, viewing angle, installation pose, and noise characteristics, which leads to low-overlap and mixed sparse–dense point cloud registration challenges. To address this issue, this paper proposes a GICP-based registration flow improvement method for heterogeneous multi-LiDAR systems used in intelligent grain warehousing robots. The method improves registration stability through overlap-region cropping, voxel downsampling, and a star-topology registration strategy, and further introduces a point-to-plane evaluation metric based on local planar models together with cross-LiDAR planar consistency verification. Experimental results show that the proposed method reduces the point-to-plane error to 0.1487 m in the L0−L1 registration task and 0.1090 m in the L1−L2 registration task, outperforming ICP, point-to-plane ICP, and NDT while maintaining acceptable computational efficiency. These results demonstrate that the method can improve structural alignment quality and provide reliable geometric support for multi-sensor perception, mapping, and autonomous operation of grain warehousing robots. Rather than proposing a fundamentally new registration mathematical model, this study proposes a highly engineered GICP-based workflow. It should be noted that the proposed workflow is specifically tailored and optimized for plane-dominated and semi-static grain storage environments, restricting its validated scope to static or low-speed multi-LiDAR registration tasks.