ICP 계산속도 향상을 위한 빠른 Correspondence 매칭 방법
A Fast Correspondence Matching for Iterative Closest Point Algorithm
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This paper considers a method of fast correspondence matching for iterative closest point (ICP) algorithm. In robotics, the ICP algorithm and its variants have been widely used for pose estimation by finding the translation and rotation that best align two point clouds. In computational perspectives, the main difficulty is to find the correspondence point on the reference point cloud to each observed point. Jump-table-based correspondence matching is one of the methods for reducing computation time. This paper proposes a method that corrects errors in an existing jump-table-based correspondence matching algorithm. The criterion activating the use of jump-table is modified so that the correspondence matching can be applied to the situations, such as point-cloud registration problems with highly curved surfaces, for which the existing correspondence-matching method is non-applicable. For demonstration, both hardware and simulation experiments are performed. In a hardware experiment using Hokuyo-10LX LiDAR sensor, our new algorithm shows 100% correspondence matching accuracy and 88% decrease in computation time. Using the F1TENTH simulator, the proposed algorithm is tested for an autonomous driving scenario with 2D range-bearing point cloud data and also shows 100% correspondence matching accuracy.
Abstract
This paper considers a method of fast correspondence matching for iterative closest point (ICP) algorithm. In robotics, the ICP algorithm and its variants have been widely used for pose estimation by finding the translation and rotation that best align two point clouds. In computational perspectives, the main difficulty is to find the correspondence point on the reference point cloud to each observed point. Jump-table-based correspondence matching is one of the methods for reducing computation time. This paper proposes a method that corrects errors in an existing jump-table-based correspondence matching algorithm. The criterion activating the use of jump-table is modified so that the correspondence matching can be applied to the situations, such as point-cloud registration problems with highly curved surfaces, for which the existing correspondence-matching method is non-applicable. For demonstration, both hardware and simulation experiments are performed. In a hardware experiment using Hokuyo-10LX LiDAR sensor, our new algorithm shows 100% correspondence matching accuracy and 88% decrease in computation time. Using the F1TENTH simulator, the proposed algorithm is tested for an autonomous driving scenario with 2D range-bearing point cloud data and also shows 100% correspondence matching accuracy.
참고문헌 (10)
- D. Hahnel, "An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements", 2003 IEEE, 2003, ~
- J. Minguez, "An architecture for sensor-based navigation in realistic dynamic and troublesome scenarios", 2004 IEEE, 2004, ~
- D. Schulz, "Tracking multiple moving targets with a mobile robot using particle filters and statistical data association", 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164) (2001)
- P.J. Besl, "A method for registration of 3-D shapes", IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), pp.239~256 (1992), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- A. Censi, "An ICP variant using a point-to-line metric", 2008IEEE International Conference on Robotics and Automation (2008)
- M. Greenspan, "Approximate k-d tree search for efficient ICP", Fourth International Conference on 3-D Digital Imaging and Modeling, 2003 (2003)
- W. -S. Choi, "Fast Iterative Closest Point framework for 3D LIDAR data in intelligent vehicle", 2012 IEEE Intelligent Vehicles Symposium (2012)
- R. Benjemaa, "Fast global registration of 3D sampled surfaces using a multi-z-buffer technique", Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134), pp.113~120 (1997)
- Daehwan Kim, "A Fast ICP Algorithm for 3-D Human Body Motion Tracking", IEEE Signal Processing Letters, 17(4), pp.402~405 (2010), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- "F1TENTH Racing Community"[URL]