Instant Object Detection in Lidar Point Clouds

In this letter, 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. First, 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. Finally, we refine the classification with contextual features considering the possible expected scene topologies. We tested our algorithm in real Lidar measurements, containing 1485 objects captured from different urban scenarios.