Journal
IET CYBER-SYSTEMS AND ROBOTICS
Volume 4, Issue 3, Pages 189-199Publisher
WILEY
DOI: 10.1049/csy2.12054
Keywords
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Funding
- National Natural Science Foundation of China [62173056, U1913201]
- Central University Basic Research Fund of China [DUT21LAB114]
- National key research and development program of China [2020YFC1511704]
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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.
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.
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