R-PCR: Recurrent Point Cloud Registration Using High-Order Markov Decision

Despite the fact that point cloud registration under noisy conditions has recently begun to be tackled by several non-correspondence algorithms, they neither struggle to fuse the global features nor abandon early state estimation during the iterative alignment. To solve the problem, we propose a nov...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-04, Vol.15 (7), p.1889
Main Authors: Cheng, Xiaoya, Yan, Shen, Liu, Yan, Zhang, Maojun, Chen, Chen
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Benchmarks
Comparative analysis
Datasets
high-order markov decision
large-scale point cloud
Machine learning
Markov processes
Neural networks
Optimization
point cloud registration
recurrent neural network
Recurrent neural networks
Registration
State estimation
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Summary:Despite the fact that point cloud registration under noisy conditions has recently begun to be tackled by several non-correspondence algorithms, they neither struggle to fuse the global features nor abandon early state estimation during the iterative alignment. To solve the problem, we propose a novel method named R-PCR (recurrent point cloud registration). R-PCR employs a lightweight cross-concatenation module and large receptive network to improve global feature performance. More importantly, it treats the point registration procedure as a high-order Markov decision process and introduces a recurrent neural network for end-to-end optimization. The experiments on indoor and outdoor benchmarks show that R-PCR outperforms state-of-the-art counterparts. The mean average error of rotation and translation of the aligned point cloud pairs are, respectively, reduced by 75% and 66% on the indoor benchmark (ScanObjectNN), and simultaneously by 50% and 37.5% on the outdoor benchmark (AirLoc).
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15071889