A teleoperated system in a robot greatly reduces the demands on the human operator, although some human intervention is still required to perform such tasks as insulator recognition, positional adjustments of the robot, and guidance toward electric lines and insulators. In order to automate some of the robot's capabilities, we have developed a 3D object-localization method for the robot's positional adjustment. The method is designed to be insensitive to noise, outliers and occlusions while, at the same time, it has optimal run-time efficiency. The main contribution of our algorithm is the use of an objective function which is specified to reduce the effect of noise and outliers in the range image and a method for minimizing this function. The objective function is efficiently minimized by dynamically recomputing correspondences as the pose improves. Our algorithm is general enough to be applied not only to our dual-armed mobile robots but also to other teleoperation robots. This algorithm should greatly reduce the burden of operators when applied. This paper first describes our algorithm, and then presents a performance evaluation.
['Kentaro Kawamura', 'Kiminori Hasegawa', 'Yasuyuki Someya', 'Yoichi Sato', 'Katsushi Ikeuchi']
Robust localization for 3D object recognition using local EGI and 3D template matching with M-estimators