Enabling Long Range Point Cloud Registration in Vehicular Networks via Muti-Hop Relays

Point cloud registration (PCR) can significantly extend the visual field and enhance the point density on distant objects, thereby improving driving safety. However, it is very challenging for vehicles to perform online registration between long-range point clouds. In this paper, we propose an online long-range PCR scheme in VANETs, called LoRaPCR, where vehicles achieve long-range registration through multi-hop short-range highly-accurate registrations. Given the NP-hardness of the problem, a heuristic algorithm is developed to determine best registration paths while leveraging the reuse of registration results to reduce computation costs. Moreover, we utilize an optimized dynamic programming algorithm to determine the transmission routes while minimizing the communication overhead. To the best of our knowledge, LoRaPCR is the first solution to achieve multi-vehicle point cloud long-range registration. Results of extensive experiments demonstrate that LoRaPCR can achieve high PCR accuracy with low relative translation and rotation errors of 0.55 meters and 1.43<inline-formula><tex-math notation="LaTeX">${}^{\circ }$</tex-math></inline-formula>, respectively, at a distance of over 100 meters, and reduce the computation overhead by more than 50% compared to the state-of-the-art method.

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Institutions

Publication Details

Journal
IEEE Transactions on Mobile Computing
Published
2024-08-21
DOI
https://doi.org/10.1109/tmc.2024.3446828
Citations
2
Primary Topic
Vehicular Ad Hoc Networks (VANETs)
Type
article
Field-Weighted Citation Impact
0.42

Funders

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article

Enabling Long Range Point Cloud Registration in Vehicular Networks via Muti-Hop Relays

Shan Chang, Minyi Guo, L.L. Zhang, Hongzi Zhu et al.
2 citations
IEEE Transactions on Mobile Computing
Vehicular Ad Hoc Networks (VANETs)
0.42
article

Enabling Long Range Point Cloud Registration in Vehicular Networks via Muti-Hop Relays

Shan Chang, Minyi Guo, L.L. Zhang, Hongzi Zhu, Yunxiang Cai, Zhenxi Wang, Quan Liu
article en
2 citations

Abstract

Point cloud registration (PCR) can significantly extend the visual field and enhance the point density on distant objects, thereby improving driving safety. However, it is very challenging for vehicles to perform online registration between long-range point clouds. In this paper, we propose an online long-range PCR scheme in VANETs, called LoRaPCR, where vehicles achieve long-range registration through multi-hop short-range highly-accurate registrations. Given the NP-hardness of the problem, a heuristic algorithm is developed to determine best registration paths while leveraging the reuse of registration results to reduce computation costs. Moreover, we utilize an optimized dynamic programming algorithm to determine the transmission routes while minimizing the communication overhead. To the best of our knowledge, LoRaPCR is the first solution to achieve multi-vehicle point cloud long-range registration. Results of extensive experiments demonstrate that LoRaPCR can achieve high PCR accuracy with low relative translation and rotation errors of 0.55 meters and 1.43<inline-formula><tex-math notation="LaTeX">${}^{\circ }$</tex-math></inline-formula>, respectively, at a distance of over 100 meters, and reduce the computation overhead by more than 50% compared to the state-of-the-art method.

IEEE Transactions on Mobile ComputingVol. 23(12)
Donghua University (CN), Shanghai Jiao Tong University (CN)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 39%
Vehicular Ad Hoc Networks (VANETs)
0.42
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Enabling Long Range Point Cloud Registration in Vehicular Networks via Muti-Hop Relays — Shan Chang, Minyi Guo, et al. · IEEE Transactions on Mobile Computing (2024) | TGRS Research Map | TGRS