Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (3): 581-593.doi: 10.1007/s11390-019-1928-6

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

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Geometry-Aware ICP for Scene Reconstruction from RGB-D Camera

Bo Ren, Member, CCF, ACM, Jia-Cheng Wu, Ya-Lei Lv, Ming-Ming Cheng, Member, CCF, ACM, Shao-Ping Lu, Member, CCF, ACM   

  1. College of Computer Science, Nankai University, Tianjin 300350, China
  • Received:2018-12-29 Revised:2019-03-15 Online:2019-05-05 Published:2019-05-06
  • About author:Bo Ren received his Ph.D. degree in computer science from Tsinghua University, Beijing, in 2015. He is currently a lecturer in the College of Computer Science, Nankai University, Tianjin. His research interests include physically-based simulation and rendering, scene geometry reconstruction and analysis. His recent research focuses on multi-fluid and multi-phase simulations in computer graphics.
  • Supported by:
    This work was supported by Tianjin Natural Science Foundation of China under Grant Nos. 18JCYBJC41300 and 18ZXZNGX00110, and the National Natural Science Foundation of China under Grant No. 61620106008.

The Iterative Closest Point (ICP) scheme has been widely used for the registration of surfaces and point clouds. However, when working on depth image sequences where there are large geometric planes with small (or even without) details, existing ICP algorithms are prone to tangential drifting and erroneous rotational estimations due to input device errors. In this paper, we propose a novel ICP algorithm that aims to overcome such drawbacks, and provides significantly stabler registration estimation for simultaneous localization and mapping (SLAM) tasks on RGB-D camera inputs. In our approach, the tangential drifting and the rotational estimation error are reduced by:1) updating the conventional Euclidean distance term with the local geometry information, and 2) introducing a new camera stabilization term that prevents improper camera movement in the calculation. Our approach is simple, fast, effective, and is readily integratable with previous ICP algorithms. We test our new method with the TUM RGB-D SLAM dataset on state-of-the-art real-time 3D dense reconstruction platforms, i.e., ElasticFusion and Kintinuous. Experiments show that our new strategy outperforms all previous ones on various RGB-D data sequences under different combinations of registration systems and solutions.

Key words: ICP (iterative closest point); RGB-D; tangential drifting; rotational estimation; covariance matrix;

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