Paper Info
Title
Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments.
Abstract
Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases e.g., to avoid executing longer paths, more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional 3D traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping NDT-TM representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy NDT-OM maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.
Year
DOI
Venue
2017
10.1002/rob.21657
J. Field Robotics
Field
DocType
Volume
Computer vision,Normal distribution,Simulation,Terrain,Lidar,Artificial intelligence,Mapping algorithm,Engineering,Robot,Reflectivity,Classifier (linguistics),Traverse
Journal
34
Issue
ISSN
Citations 
3
1556-4959
3
PageRank 
References 
Authors
0.46
34
3
Name
Order
Citations
PageRank
Juhana Ahtiainen141.17
Todor Stoyanov226026.07
Jari Saarinen314613.82