Planar simplification and texturing of dense point cloud maps

Abstract

Dense RGB-D based SLAM techniques and high-fidelity LIDAR scanners are examples from an abundant set of systems capable of providing multi-million point datasets. These large datasets quickly become difficult to process and work with due to the sheer volume of data, which typically contains significant redundant information, such as the representation of planar surfaces with hundreds of thousands of points. In order to exploit the richness of information provided by dense methods in real-time robotics, techniques are required to reduce the inherent redundancy of the data. In this paper we present a method for efficient triangulation and texturing of planar surfaces in large point clouds. Experimental results show that our algorithm removes more than 90% of the input planar points, leading to a triangulation with only 10% of the original amount of triangles per planar segment, improving upon an existing planar simplification algorithm. Despite the large reduction in vertex count, the principal geometric features of each segment are well preserved. In addition to this, our texture generation algorithm preserves all colour information contained within planar segments, resulting in a visually appealing and geometrically accurate simplified representation.