研究目的
Investigating the effectiveness of an ICP-based calibration method for multiple ToF depth sensors, including the use of specific calibration objects and a depth correction method for distortions.
研究成果
The proposed ICP-based calibration method with specific calibration objects and depth compensation effectively calibrates multiple ToF depth sensors, achieving higher accuracy than traditional 2D pattern-based methods. The method is particularly suited for applications requiring precise 3D modeling from multiple sensors.
研究不足
The method requires specific calibration objects and may not perform well with arbitrary objects. The depth correction method compensates for global distortions but may not fully address per-pixel distortions, especially near image boundaries.
1：Experimental Design and Method Selection:
The study utilizes ICP for registering depth maps from multiple sensors, with modifications to handle the large distances between sensors. Specific calibration objects are used to enhance matching ability.
2：Sample Selection and Data Sources:
Depth maps are captured using Kinect V2 sensors arranged around an object. Calibration objects with known geometries are used to facilitate accurate registration.
3：List of Experimental Equipment and Materials:
Kinect V2 sensors, specific calibration objects (hemispheres, cylinders, truncated cones), and a plane object for placing calibration objects.
4：Experimental Procedures and Operational Workflow:
Depth maps are captured, transformed to point clouds, trimmed, and down-sampled. ICP is applied with initialization from a blueprint of sensor setups. A depth correction method is applied to compensate for distortions.
5：Data Analysis Methods:
Reprojection errors between input point clouds and projected point clouds are measured using root mean square error (RMSE). The effectiveness of the calibration method is evaluated based on these errors.
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