Multiple Sensor Based Terrain Classification

Publisher:
Australasion Conference on Robotics and Automation
Publication Type:
Conference Proceeding
Citation:
Australasion Conference on Robotics and Automation, 2011, pp. 1 - 7
Issue Date:
2011-01
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In the context of road vehicles, knowledge of terrain types is useful for improving passenger safety and comfort. This paper presents a method for terrain classification based on multiple sensors including an accelerometer, wheel encoders and a camera. The vertical accelerations and the speed of the vehicle together with a dynamic vehicle model are used to predict the road profile. Features extracted from the road profile are fused with image features to produce a speed invariant feature set. A supervised learning algorithm based on Neural network (NN) is used to classify different road types. Experiments carried out on an instrumented road vehicle (CRUISE), by manually driving on a variety of road types at different speeds are presented to demonstrate that the fusion of multiple sensory cues can significantly improve the road type classification accuracy.
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