On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM
Institute of Robotics and Machine Intelligence, Poznan University of Technology（波兹南工业大学机器人与机器智能研究所）
英文摘要：
We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments. A new architecture of the deep neural network is presented that learns the visual context acquired from synthetic LiDAR intensity images. This approach allows a single multi-beam LiDAR to produce rich and highly descriptive location signatures. The method is tested on two public datasets, demonstrating an improved descriptiveness of the new descriptors, and more reliable loop closure detection in SLAM. Attention analysis of the network is used to show the importance of focusing on the broader context rather than only on the 3-D segment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
