Terrain Mapping and Classification in Outdoor Environments Using Neural Networks

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Executive Summary

This paper describes a three-dimensional terrain mapping and classification technique to allow the operation of mobile robots in outdoor environments using laser range finders. The authors propose the use of a multi-layer perceptron neural network to classify the terrain into navigable, partially navigable, and non-navigable. The maps generated by their approach can be used for path planning, navigation, and local obstacle avoidance. Experimental tests using an outdoor robot and a laser sensor demonstrate the accuracy of the presented methods. Mobile robotics is a field of robotics focused on the development of devices capable of moving autonomously to perform predetermined tasks. In order to navigate safely, robots use perception sensors like Laser Range Finders (LRF) and cameras to get information from the environment and detect potential obstacles.

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