Abstract-In this paper we present a new approach for object classification in continuously streamed Lidar point clouds collected from urban areas. The input of our framework is raw 3-D point cloud sequences captured by a Velodyne HDL-64 Lidar, and we aim to extract all vehicles and pedestrians in the neighborhood of the moving sensor. We propose a complete pipeline developed especially for distinguishing outdoor 3-D urban objects. Firstly, we segment the point cloud into regions of ground, short objects (i.e. low foreground) and tall objects (high foreground). Then using our novel two-layer grid structure, we perform efficient connected component analysis on the foreground regions, for producing distinct groups of points which represent different urban objects. Next, we create depth-images from the object candidates, and apply an appearance based preliminary classification by a Convolutional Neural Network (CNN). Finally we refine the classification with contextual features considering the possible expected scene topologies. We tested our algorithm in real Lidar measurements, containing 1159 objects captured from different urban scenarios.
Abstract. Efficient and fast object detection from continuously streamed 3-D point clouds has a major impact in many related research tasks, such as autonomous driving, self localization and mapping and understanding large scale environment. This paper presents a LIDAR-based framework, which provides fast detection of 3-D urban objects from point cloud sequences of a Velodyne HDL-64E terrestrial LIDAR scanner installed on a moving platform. The pipeline of our framework receives raw streams of 3-D data, and produces distinct groups of points which belong to different urban objects. In the proposed framework we present a simple, yet efficient hierarchical grid data structure and corresponding algorithms that significantly improve the processing speed of the object detection task. Furthermore, we show that this approach confidently handles streaming data, and provides a speedup of two orders of magnitude, with increased detection accuracy compared to a baseline connected component analysis algorithm.
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