Environment interpretation for autonomous indoor navigation of micro air vehicles

In this paper, indoor environment classification and interpretation algorithm is proposed. Proposed algorithm needs low computation power and low payload thus enabling micro air vehicle (MAV) to quickly react and navigate. Here indoor environment is classified into corridor, staircase, and open space by using image edge gist descriptors and a neural network classifier. Use of some predetermined thresholds further increases the confidence of the classification and interpretation algorithm. Detection of horizontal lines cluster and vanishing point is used for the navigation in staircase and corridor environment respectively. Results demonstrate that the proposed algorithm can interpret the indoor environment effectively with > 90% accuracy.