Markov random field terrain classification of large-scale 3D maps

Abstract

Simultaneous localization and mapping, drivability classification of the terrain and path planning represent three major research areas in the field of autonomous outdoor robotics. Especially unstructured environments require a careful examination as they are unknown, continuous and the number of possible actions for the robot are infinite. We present an approach to create a semantic 3D map with drivability information for wheeled robots using a terrain classification algorithm. Our robot is equipped with a 3D laser range finder, a Velodyne HDL-64E, as primary sensor. For the registration of the point clouds, we use a featureless 3D correlative scan matching algorithm which is an adaption of the 2D algorithm presented by Olson. Every 3D laser scan is additionally classified with a Markov random field based terrain classification algorithm. Our data structure for the terrain classification approach is a 2D grid whose cells carry information extracted from the laser range finder data. All cells within the grid are classified and their surface is analyzed regarding its drivability for wheeled robots. The main contribution of this work is the novel combination of these two algorithms which yields classified 3D maps with obstacle and drivability information. Thereby, the newly created semantic map is perfectly tailored for generic path planning applications for all kinds of wheeled robots. We evaluate our algorithms on large datasets with more than 137 million annotated 3D points that were labeled by multiple human experts. All datasets are published online and are provided for the community.