Sequence matching enhanced 3D place recognition using candidate rearrangement

Deep-learning-based 3D place recognition has received more attention since the data-driven fashion is widely used for the 3D point cloud applications. Most of the existing deep-learning-based 3D place recognition methods only utilise a single scene for place recognition. However, a single scene may have measurement noise or observable dynamic object differences, which may lead to a reduction in recognition accuracy. To improve the performance of 3D place recognition, a sequence matching based rearrangement method is proposed. Our sequence matching method is based on an assignment algorithm and guides the candidate rearrangement in searching for a similar place. The global descriptor extraction adapts the effective sparse tensor representation and a simple pooling layer to obtain the global descriptor. A new loss function combination is employed to train the network. The proposed approach is evaluated on the popular 3D place recognition benchmarks, which proves the effectiveness of the proposed approach.

Automated summary

This study proposed a deep-learning-based method for 3D place recognition, which enhances recognition accuracy through sequence matching-based rearrangement and global descriptor extraction.