Affiliation:
1. College of Systems Engineering, National University of Defense Technology, Changsha 410000, China
Abstract
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).
Subject
General Earth and Planetary Sciences
Cited by
2 articles.
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