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We propose an extension to the segment-based global localization method forLiDAR SLAM using descriptors learned considering the visual context of thesegments. A new architecture of the deep neural network is presented thatlearns the visual context acquired from synthetic LiDAR intensity images. Thisapproach allows a single multi-beam LiDAR to produce rich and highlydescriptive location signatures. The method is tested on two public datasets,demonstrating an improved descriptiveness of the new descriptors, and morereliable loop closure detection in SLAM. Attention analysis of the network isused to show the importance of focusing on the broader context rather than onlyon the 3-D segment.

On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM