Abstract
In this thesis, we propose a system for automatic object detection and pose estimation from a single depth map containing multiple objects for robot applications. The proposed object detection algorithm is based on matching the keypoints extracted from the depth image by using the proposed geometry-based RANSAC algorithm with the FPFH descriptor. The keypoint detection method used in this work is extended from the 2D Harris corner detector to the 3D Harris corner detector. Then, similar corresponding points with FPFH feature are extracted based on their distance. The proposed geometry-based RANSAC algorithm integrates the characteristics of the geometry to choose the inliers from similar corresponding points. In the proposed system, we combine the keypoint detection and the geometry-based RANSAC algorithm to detect the objects, followed by the ICP algorithm to refine the 3D object alignment. We exploit the corresponding points to calculate the rigid transformation for pose estimation. In the experimental results, simulated and real world depth data are shown to demonstrate the accuracy of pose estimation by using the proposed system.