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This paper describes a vision-based navigation method in an indoor environment for an autonomous mobile robot which can avoid obstacles. In this method, the self-localization of the robot is done with a model-based vision system, and nonstop navigation is realized by a retroactive position correction system. Stationary obstacles are avoided with single-camera vision and moving obstacles are detected with ultrasonic sensors. We will report on experiments in a hallway using the YAMABICO robot.

Vision-based bin-picking is increasingly more dicult as the complexity of target objects increases. We propose an e cient solution where c omplex objects are su ciently represented b y s i m p l e f e atures/cues thus invariance to object complexity is established. The region extraction algorithm utilized in our approach is capable of providing the focus of attention to the simple cues as a trigger toward r ecognition and pose estimation. Successful bin-picking experiments of industrial objects using stereo vision tools are p r esented.

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