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Classification of the indoor environment of a mobile robot using principal component analysis

Yaqub T., Katupitya J.

Journal of Automation Mobile Robotics and Intelligent Systems

2008

Vol. 2, No. 2

44-53

Large indoor environments of a mobile robot usually consist of different types of areas connected together. The structure of a corridor differs from a room, a main hall or laboratory. A method for online classification of these areas using a laser scanner is presented in this paper. This classification can reduce the search space of localization module to a great extent making the navigation system efficient. The intention was to make the classification of a sensor observation in a fast and real-time fashion and immediately on its arrival in the sensor frame. Our approach combines both the feature based and statistical approaches. We extract some vital features of lines and corners with attributes such as average length of lines and distance between corners from the raw laser data and classify the observation based on these features. Bootstrap method is used to get a robust correlation of features from training data and finally Principal Component Analysis (PCA) is used to model the environment. In PCA, the underlying assumption is that data is coming from a multivariate normal distribution. The use of bootstrap method makes it possible to use the observations data set which set, which is not necessarily normally distributed. This technique lifts up the normality assumption and reduces the computational cost further as compared to the PCA techniques based on raw sensor data and can be easily implemented in moderately complex indoor environment. The knowledge of the environment can also be up-dated in an adaptive fashion. Results of experimentation in a simulated hospital building under varying environmental conditions using a real-time robotic software Player/Stage are shown.