抄録
A map generated from ground-based threedimensional LIDAR data is a critical component for autonomous vehicle navigation using a vision based sensor. When the size of a map is large and the number of grid cells is relatively big, managing the map associated with a dense data set from 3D LIDAR scanner is a demanding task. Wavelets serve as the basis for an efficient compression scheme which makes it possible to significantly reduce processing effort to generate and manage a grid map in real-time. This research proposes a novel approach to generate an occupancy map from compressed measurement signals. A one-dimensional Haar wavelet transform has been applied to compress 3D scan data, from which occupancy maps have been generated. Our experimental results show that this method performs well to provide an autonomous vehicle with rich 3D environment information. As an alternative method to reduce 3D scan data, Compressive Sensing is also adopted in this research. Compressive Sensing uses a small sub set of data to recover the unknown original data. Utilization of Compact Sensing might lead us to the development of a favorable sensing system; however, conventional compressive sensing recovery algorithms might require a non-trivial amount of computing. With sparse Bayesian learning algorithm, compressive sensing can provide a fast and cost effective solution for acquisition systems characterized by large data set.
