Performance of global descriptors for velodyne-based urban object recognition

Object Recognition is an essential component for Autonomous Land Vehicle (ALV) navigation in urban environments. This paper presents a thorough evaluation of the performance of some state of the art global descriptors on the public Sydney Urban Objects Dataset 1 , which was collected in the Central Business District of Sydney. These descriptors are Bounding Box descriptor, Histogram of Local Point Level descriptor, Hierarchy descriptor, and Spin Image (SI). We also propose a novel Global Fourier Histogram (GFH) descriptor. Experimental results on the public data set show that GFH descriptor turns out to be one of the best global descriptors for the object recognition in urban environments, and the results on the data collected by our own ALV in urban environments also demonstrate its usefulness.