Abstract
Point set registration has been a topic of significant research interest in the field of mobile intelligent unmanned systems. In this paper, we present a novel approach for a three-dimensional scan-to-map point set registration. Using Gaussian process (GP) regression, we propose a new type of map representation, based on a regionalized GP map reconstruction algorithm. We combine the predictions and the test locations derived from the GP as the predictive points. In our approach, the correspondence relationships between predictive point pairs are set up naturally, and a rigid transformation is calculated iteratively. The proposed method is implemented and tested on three standard point set datasets. Experimental results show that our method achieves stable performance with regard to accuracy and efficiency, on a par with two standard methods, the iterative closest point algorithm and the normal distribution transform. Our mapping method also provides a compact point-cloud-like map and exhibits low memory consumption.
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Bo LI and Yu ZHANG designed the research. Bo LI, Yu ZHANG, and Wen-jie ZHAO processed the data. Bo LI designed the computer programs and drafted the manuscript. Yu ZHANG, Wen-jie ZHAO, and Ping LI helped organize the manuscript. Bo LI, Yu ZHANG, and Wen-jie ZHAO revised and finalized the paper.
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Bo LI, Yu ZHANG, Wen-jie ZHAO, and Ping LI declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (Nos. 61673341, 61703366, and 11705026)
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Li, B., Zhang, Y., Zhao, Wj. et al. Novel 3D point set registration method based on regionalized Gaussian process map reconstruction. Front Inform Technol Electron Eng 21, 760–776 (2020). https://doi.org/10.1631/FITEE.1900457
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DOI: https://doi.org/10.1631/FITEE.1900457