다중센서 융합 상이 지도를 통한 다중센서 기반 3차원 복원 결과 개선
Refinements of Multi-sensor based 3D Reconstruction using a Multi-sensor Fusion Disparity Map
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초록
This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (Φ, Δ ) and a camera calibration matrix (K ). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.
Abstract
This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (Φ, Δ ) and a camera calibration matrix (K ). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.
참고문헌 (6)
- Alastair Harrison, "High Quality 3D Laser Ranging Under General Vehicle Motion" (2008), IEEE International Conference on Robotics and Automation
- Heiko Hirschmuller, "Real-Time Correlation-Based Stereo Vision with Reduced Border Errors", International Journal of computer Vision, 47 (2002)
- Kiana Hajebi, "Dense Surface from Infrared Stereo" (2007), IEEE Workshop on Applications of Computer Vision
- J. H. Joung, "3D terrain Reconstruction Using 2D Laser Range Finder and Camera Based on Cubic Grid" (2008), International Conference on Ubiquitous Robots and Ambient Intelligence
- Z. Zhang, "A Flexible New Technique for Camera Calibration", IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11) (2000)
- A. Zhang, "Extrinsic Calibration of a Camera and Laser Range Finder (improves camera calibration)", pp.2004, Proceedings of the IEEE