期刊
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
卷 -, 期 -, 页码 5654-5660出版社
IEEE
DOI: 10.1109/ICRA48506.2021.9560932
关键词
-
资金
- EPSRC FAIR-SPACE Hub [EP/R026092/1]
- Royal Society [RGS202432]
- EU Horizon 2020 ILIAD [732737]
This paper introduces a novel approach named NDT-Transformer for real-time and large-scale place recognition using 3D point clouds. By utilizing the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with geometrical and contextual information, achieving significant improvements in place recognition performance.
3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loopclosure detection) in lidar-based SLAM systems. This paper proposes a novel approach, named NDT-Transformer, for real-time and large-scale place recognition using 3D point clouds. Specifically, a 3D Normal Distribution Transform (NDT) representation is employed to condense the raw, dense 3D point cloud as probabilistic distributions (NDT cells) to provide the geometrical shape description. Then a novel NDT-Transformer network learns a global descriptor from a set of 3D NDT cell representations. Benefiting from the NDT representation and NDT-Transl'ormer network, the learned global descriptors are enriched with both geometrical and contextual information. Finally, descriptor retrieval is achieved using a query-database for place recognition. Compared to the state-of-the-art methods, the proposed approach achieves an improvement of 7.52% on average top 1 recall and 2.73% on average top 1% recall on the Oxford Robotcar benchmark.
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