Abstract
A planar surface extraction method is proposed for the indoor navigation of a micro-air vehicle (MAV). The algorithm finds planar clusters from the unorganized pointclouds. This is achieved by implementing a novel approach that first segments the data points into clusters and then each cluster is estimated for its planarity. The method is tested on indoor point cloud data obtained by 3D PrimeSense based sensor. In order to validate the algorithm, a simulated model containing a set of planes has been constructed, with noise injected into the model. The results of the empirical evaluation suggest that the method performs well even in the presence of the noise and non-planar objects, suggesting that the method will be a viable one for use in MAV navigation in the presence of noisy sensor data.
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