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In this work, we introduce a novel multimodal descriptor, the image-assisting binary and triangle combined (iBTC) descriptor, which fuses LiDAR (Light Detection and Ranging) and camera measurements for 3D place recognition. The inherent invariance of a triangle to rigid transformations inspires us to design triangle-based descriptors. We first extract distinct 3D key points from both LiDAR and camera measurements and organize them into triplets to form triangles. By utilizing the lengths of the sides of these triangles, we can create triangle descriptors, enabling the rapid retrieval of similar triangles from a database. By encoding the geometric and visual details at the triangle vertices into binary descriptors, we augment the triangle descriptors with richer local information. This enrichment process empowers our descriptors to reject mis-matched triangle pairs. Consequently, the remaining matched triangle pairs yield accurate loop closure place indices and relative poses. In our experiments, we conduct a thorough comparison of our proposed method with several SOTA methods across public and self-collected datasets. The results demonstrate that our method exhibits superior performance in place recognition and overcomes the limitations associated with the unimodal methods like BTC, RING++, ORB-DBoW2, and NetVLAD. Additionally, we perform a time cost benchmark experiment and the result indicates that our method's time consumption is reasonable, compared with baseline methods.
Zou et al. (Thu,) studied this question.