Li, L;
Yang, M;
(2021)
Point Cloud Registration Based on Direct Deep Features With Applications in Intelligent Vehicles.
IEEE Transactions on Intelligent Transportation Systems
10.1109/tits.2021.3123619.
(In press).
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Abstract
Point cloud registration is widely used in the research of intelligent vehicles, typical problems include map matching, visual odometer, pose estimation, etc. This paper proposes a deep learning-based registration method that can input point clouds directly, thereby preventing information loss of preprocessing needed by alternative deep-learning approaches. Our network, named DPFNet (Direct Point Feature Net), gradually downsamples the point cloud and aggregates points around determined reference points to formulate local features automatically. This is facilitated by a novel convolution-like operator and a novel loss function. The points in the point cloud are mapped to a high dimensional embedding through the designed deep neural network, where every embedding reflects the local feature of a specific spatial area. Based on the embedding features, correspondences between points can be estimated robustly and the registration between the point clouds can be obtained using an external geometric optimization algorithm. Experimental results on open benchmarks validate the proposed method and show that its performance is favourable over several baseline methods. Specifically, we test the proposed algorithm on KITTI benchmark, which shows its potential in tasks of intelligent vehicles, e.g., map matching, visual or LiDAR odometer.
| Type: | Article |
|---|---|
| Title: | Point Cloud Registration Based on Direct Deep Features With Applications in Intelligent Vehicles |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tits.2021.3123619 |
| Publisher version: | https://doi.org/10.1109/tits.2021.3123619 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Feature extraction, Deep learning, Three-dimensional displays, Neural networks, Intelligent vehicles, Histograms, Task analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10137680 |
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