Real-time Corridor Recognition for Autonomous Vehicle

Recognition of a working environment is critical for an autonomous vehicle such as a mobile robot to guide it along corridor and to confirm its possible intelligence. Therefore it is necessary to equip a recognition system with sensor that collect environmental information. As an effective sensor a CCD camera is generally useful for all kinds of mobile robots. However, it is hard to use the CCD camera for visual feedback since it requires to acquire information in real-time, and moreover to be robust against lighting condition varieties. This research presents a corridor recognition method using unprocessed gray-scale image, termed a raw image, and a genetic algorithm (GA), without any image information conversion, to conduct the recognition process in real-time. To achieve robustness concerning lighting condition varieties, we propose a model-matching method using a representative object model designated here as surface-strips model. The robustness of the method against noise in the environment, including lighting conditions variations, and the effectiveness of the method for real-time recognition have been verified using real corridor images.