In this paper, we describe a novel technique to extract a polyhedral description from panoramic range data of a scene taken by a panoramic laser range finder. First, we introduce a reasonable noise model of the range data acquired with a laser radar range finder, and derive a simple and efficient approximate solution of the optimal fitting of a local plane in the range data under the assumed noise model. Then, we compute the local surface normals using the proposed method and extract stable planar regions from the range data by using both the distribution information of local surface normals and their spatial information in the range image. Finally, we describe a method which builds a polyhedral description of the scene using the extracted stable planar regions of the panoramic range data with 360 field of view in a polar coordinate system. Experimental results on complex real range data show the effectiveness of the proposed method.

Location of a source is of considerable interest in wireless sensor networks, and it can be estimated from passive measurements of the arrival times. A novel algorithm for source location by utilizing the time of arrival (TOA) measurements of a signal received at spatially separated sensors is proposed. The algorithm is based on total least-squares (TLS) method, which is a generalized least-squares method to solve an overdetermined set of equations whose coefficients are noisy, and gives an explicit solution. Comparisons of performance with standard least-squares method are made, and Monte Carlo simulations are performed. Simulation results indicate that the proposed TLS algorithm gives better results than LS algorithm.

In this paper, we propose a probabilistic approach to derive an approximate polyhedral description from range data. We first compare several least-squares-based methods for estimation of local normal vectors and select the most robust one based on a reasonable noise model of the range data. Second, we extract the stable planar regions from the range data by examining the distributions of the local normal vectors together with their spatial information in the 2D range image. Instead of segmenting the range data completely, we use only the geometries of the extracted stable planar regions to derive a polyhedral description of the range data. The curved surfaces in the range data are approximated by their extracted plane patches. With a probabilistic approach, the proposed method can be expected to be robust against the noise. Experimental results on real range data from different sources show the effectiveness of the proposed method.