Abstract
The ICP algorithm has been widely used for point cloud registration, but it has low computational efficiency under large-scale point cloud data. Moreover, it only uses the structural information of point cloud in the solving process.
Its accuracy and efficiency will be greatly affected in scenes lacking of obvious structural features and scenes with low overlap. In this paper, we propose a semi-dense ICP algorithm based on the neighborhood of SIFT feature points. Instead of the whole points or the sparse SIFT feature points, it selects the neighborhood of SIFT feature points as the matching range of the nearest matched point pair. We also introduce a new objective function with adaptive weights for the ICP algorithm, which balances the importance of the structural features and image features by dynamically adjusting the weights, and ensures that the ICP iteration can converge correctly. Experimental results show that the proposed method achieves higher accuracy and efficiency than several related methods. Especially, its effectiveness is also verified in scenes without obvious structural features or texture features and scenes with low overlap rate of two frame point clouds.
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Hong, Y., Li, W., Chen, Y. (2022). Adaptive ICP Registration Algorithm Based on the Neighborhood of SIFT Feature Points. In: Zu, Q., Tang, Y., Mladenovic, V., Naseer, A., Wan, J. (eds) Human Centered Computing. HCC 2021. Lecture Notes in Computer Science, vol 13795. Springer, Cham. https://doi.org/10.1007/978-3-031-23741-6_3
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DOI: https://doi.org/10.1007/978-3-031-23741-6_3
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