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Keywords:
point cloud; transformation; projection; rotation; translation
point cloud; transformation; projection; rotation; translation
Summary:
3D pose estimation algorithms have been the subject of widely studied research topic due to problems related to their reliability and precision in related applications. Despite numerous studies by researchers to attempt efficient solutions to application related problems, many proposed methods still not submit sufficiently recover estimates for practical, real-world scenarios in the field of Computer Vision. Therefore, we made extensive study and presented an innovative and practical method that enables a cheap and practical solution by integrating information from both depth and color cameras. Outlier points can impact the pose estimations. We additionally implemented outlier rejection method due to outliers coming from depth to color point projection. After applying and evaluating our proposed algorithm on a public dataset for pose estimation problem, we have shown that it significantly enhances the robustness and accuracy of pose estimation in six degrees of freedom (6-DoF).
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