Appearance-Based Map Learning for Mobile Robot by Using Generalized Regression Neural Network

Authors:
Ke Wang;Wei Wang;Yan Zhuang
Affiliations:
Research Center of Information and Control, Dalian University of Technology, 116024 Dalian, China;Research Center of Information and Control, Dalian University of Technology, 116024 Dalian, China;Research Center of Information and Control, Dalian University of Technology, 116024 Dalian, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

Regression analysis between features of high-dimension is receiving attention in environmental learning of mobile robot. In this paper, we propose a novel framework, namely General regression neural network (GRNN), for approximating the functional relationship between high-dimensional map features and robot's states. We firstly adopt PCA to preprocess images taken from omnidirenctional vision. The method extracts map features optimally and reduces the correlated features while keeping the minimum reconstruction error. Then, the robot states and corresponding features of the training panoramic snapshots are used to train the given neural network. This enables robot to memorize the environmental features as well as to predict available scene given its location information. Experimental results are shown finally.