Strip Histogram Grid for Efficient LIDAR Segmentation from Urban Environments

Abstract

As part of a large-scale 3D recognition system for LIDAR data from urban scenes, we describe an approach for segmenting millions of points into coherent regions that ideally belong to a single real-world object. Segmentation is crucial because it allows further tasks such as recognition, navigation, and data compression to exploit contextual information. A key contribution is our novel Strip Histogram Grid representation that encodes the scene as a grid of vertical 3D population histograms rising up from the locally detected ground. This scheme captures the nature of the real world, thereby making segmentation tasks intuitive and efficient. Our algorithms work across a large spectrum of urban objects ranging from buildings and forested areas to cars and other small street side objects. The methods have been applied to areas spanning several kilometers in multiple cities with data collected from both aerial and ground sensors exhibiting different properties. We processed almost a billion points spanning an area of 3.3 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in less than an hour on a regular desktop.