Segmentation of dense range information in complex urban scenes

In this paper, an algorithm to segment 3D points in dense range maps generated from the fusion of a single optical camera and a multiple emitter/detector laser range finder is presented. The camera image and laser range data are fused using a Markov Random Field to estimate a 3D point corresponding to each image pixel. The textured 3D dense point cloud is segmented based on evidence of a boundary between regions of the textured point cloud. Clusters are discriminated based on Euclidean distance, pixel intensity and estimated surface normal using a fast, deterministic and near linear time segmentation algorithm. The algorithm is demonstrated on data collected with the Cornell University DARPA Urban Challenge vehicle. Performance of the proposed dense segmentation routine is evaluated in a complex urban environment and compared to segmentation of the sparse point cloud. Results demonstrate the effectiveness of the dense segmentation algorithm to avoid over-segmentation better than incorporating color and surface normal data in the sparse point cloud.