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  • 學位論文

創新式多維資訊映射整合策略運用於線上機器人於三維空間與輻射分佈偵測之研究

In-situ clouds-powered 3-D radiation detection and localization using novel color-depth-radiation (CDR) mapping

指導教授 : 陳亮嘉

摘要


The article presents an in-situ clouds-powered radioactive source detection and localization approach, namely color-depth-radiation (CDR) Mapping, using 3-D land mapping within hazardous indoor environment and incorporating sensor fusion between a RGB-D camera and a portable radiation detector. In the approach, to achieve fast and robust image registration, color images detected by the camera are initially employed to extract crucial visual features and establish pairs of matched image features between successive scanned images. Following this, matched features are incorporated with the corresponding calibrated depth information to generate 3-D keypoint cloud pairs. To remove potential noises in the acquired datasets, a novel geometric-based filtering algorithm is developed to reject incorrect keypoint pairs prior to ICP-based image registration. Most importantly, an algorithm to determine the radioactive sources’ parameters including strength and 3-D position is developed for accurate radioactive source detection and localization. With this, the radioactive sources can be accurately pinpointed in the established 3-D map for efficient contamination control and safety management. Two radiation testing experiments were performed to verify the feasibility of the approach and its detection accuracy. The simulation results indicate that the proposed approach can reach up to 95% accuracy of radiation source localization incorporated in the 3-D map.

並列摘要


The article presents an in-situ clouds-powered radioactive source detection and localization approach, namely color-depth-radiation (CDR) Mapping, using 3-D land mapping within hazardous indoor environment and incorporating sensor fusion between a RGB-D camera and a portable radiation detector. In the approach, to achieve fast and robust image registration, color images detected by the camera are initially employed to extract crucial visual features and establish pairs of matched image features between successive scanned images. Following this, matched features are incorporated with the corresponding calibrated depth information to generate 3-D keypoint cloud pairs. To remove potential noises in the acquired datasets, a novel geometric-based filtering algorithm is developed to reject incorrect keypoint pairs prior to ICP-based image registration. Most importantly, an algorithm to determine the radioactive sources’ parameters including strength and 3-D position is developed for accurate radioactive source detection and localization. With this, the radioactive sources can be accurately pinpointed in the established 3-D map for efficient contamination control and safety management. Two radiation testing experiments were performed to verify the feasibility of the approach and its detection accuracy. The simulation results indicate that the proposed approach can reach up to 95% accuracy of radiation source localization incorporated in the 3-D map.

參考文獻


12) Figure B. 12 Remotely operated and autonomous mapping system (ROAMS) [85]. 23
45) Figure B. 45 Geometry of the radiological point source localisation problem 80
54) Figure C. 9 Flowchart of the developed CDR mapping approach for in-situ clouds-powered radioactive source detection and localization using 3-D land mapping. 95
73) Figure D. 12 Accuracy of radiation source localization for the single source case and the multi source case. 118
1) Figure B. 1 Target environments for 3-D mapping. 9

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