Landmark detection by a rotary laser scanner for autonomous robot navigation in sewer pipes
نویسندگان
چکیده
This paper demonstrates the successful autonomous classification of rotary laser scanner data retrieved by an autonomous robot traversing a sewer pipe system. Rotary laser scanner serve dual purposes of fault detection and navigation. The present aim was to devise a robust landmark detection method for use in the robot’s navigation system. A standard feed forward neural network with 6 hidden neurons was trained on the first 15 principle components of the combined amplitudes of the reflected signal and distance values data. The overall system shows a 89% performance for the classification for the validation data as opposed to 79% of a linear neural network. These results demonstrate that the appropriate application of conventional sensor feature extraction, and classification methods may be used to quickly build an effective and computationally efficient landmark detection system for a mobile robot.
منابع مشابه
Navigation of an Autonomous Sewer Inspection Robot Based on Stereo Camera Images and Laser Scanner Data
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