Quality Assessment of Traversability Maps from Aerial LIDAR Data for an Unmanned Ground Vehicle

In this paper we address the problem of assessing quantitatively the quality of traversability maps computed from data collected by an airborne laser range finder. Such data is used to plan paths for an unmanned ground vehicle (UGV) prior to the execution of long range traverses. Little attention has been devoted to the problem we address in this paper. We use a unique data set of geodetic control points, real robot navigation data, ground LIDAR (LIght Detection And Ranging) data and aerial imagery, collected during a week long demonstration to support our work

Experimental Results in Using Aerial LADAR Data for Mobile Robot Navigation

In this paper, we investigate the use of high resolution aerial LADAR data for autonomous mobile robot navigation in natural environments. The use of prior maps from aerial LADAR (LAser Detection And Ranging) survey is considered for enhancing system performance in two areas. First, the prior maps are used for registration with the data from the robot in order to compute accurate localization in the map. Second, the prior maps are used for computing detailed traversability maps that are used for planning over long distances. Our objective is to assess the key issues in using such data and to report on a first batch of experiments in combining high-resolution aerial data and on-board sensin

Parts-based 3D object classification

This paper presents a parts-based method for classifying scenes of 3D objects into a set of pre-determined object classes. Working at the part level, as opposed to the whole object level, enables a more flexible class representation and allows scenes in which the query object is significantly occluded to be classified. In our approach, parts are extracted from training objects and grouped into part classes using a hierarchical clustering algorithm. Each part class is represented as a collection of semi-local shape features and can be used to perform pan class recognition. A mapping from part classes to object classes is derived from the learned part classes and known object classes. At run-time, a 3D query scene is sampled, local shape features are computed, and the object class is determined using the learned pan classes and the pan-to-object mapping. Classifying novel 3D scenes of vehicles into eight classes demonstrate the approach