Appearance-based Object Pose Estimation and Misestimation Detection by Shape Fitness
Ryo NISHIKAWA, Haruka Noguchi, Taro YAMAZAKI, Akio Nakamura · Journal of the Japan Society for Precision Engineering · 2013
We propose a system for pose estimation of an object and misestimation detection. The system estimates object pose by exploring a three-layered search tree whose node is a model image of appearance. The model images are generated from different points of view using a 3D object model in advance. Matching between model and object images is performed using SIFT feature and RANSAC. However, the pose estimation sometimes fails, especially due to occlusion. Hence, the system evaluates the result of pose estimation by shape fitness using weighted voxels. Experimental results show the basic validity of the system. The system detects misestimation even if the object is partially occluded by other objects.