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Adaptive Iterative Control Optimization ICP Algorithm for Robust Point Cloud Registration in Urban Environments
- 1 Sino-European School of Technology of Shanghai, Shanghai University, Shanghai, 200444
- 2 Houston International Institute, Dalian Maritime University, Dalian, 116026
- 3 High School Attached to Shandong Normal University, Jinan, 250014
* Author to whom correspondence should be addressed.
Abstract
This paper puts forward an improved ICP algorithm to improve the robustness of point cloud registration in complex urban environment, and adopts adaptive iterative control to solve the limitations of traditional ICP algorithm such as premature convergence or over-fitting caused by static iteration. Sobel convolution enhances the response ability of the algorithm to the complexity of the environment, and dynamically adjusts the iteration limit according to the feature difference of the point cloud, thus improving the registration accuracy and calculation efficiency. According to a large number of experiments on KITTI dataset, compared with the traditional ICP method, the algorithm can effectively reduce the root mean square error and improve the registration accuracy. These results verify the effectiveness and robustness of the algorithm in autonomous driving, robot navigation and urban mapping.
Keywords
Synchronous Positioning and Map Construction, Point Cloud Registration, Iterative Nearest Point, Adaptive Iterative Control, Sobel Convolution
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Cite this article
Zhu,T.;Mao,Y.;Zhang,J. (2025). Adaptive Iterative Control Optimization ICP Algorithm for Robust Point Cloud Registration in Urban Environments. Applied and Computational Engineering,132,83-94.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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