Learning Traversability from Point Clouds in Challenging Scenarios
Journal article, 2017

This paper aims at evaluating the capabilities to detect road traversability in urban and extra-urban scenarios of support vector machine-based classifiers that use local descriptors extracted from point cloud data. The evaluation of the proposed classifiers is carried out by using four different kernels and comparing five point descriptors obtained from geometric and appearance-based features. A comparison among the performance of descriptors individually has demonstrated that the normal vector-based descriptor achieves an accuracy of 88%, outperforming by about 6%–15% all the other considered ones. To further improve the interpretation capabilities, the space of features is augmented by merging the components of each point descriptor, reaching 92% classification accuracy. A set of test scenarios have been acquired during an extensive experimental campaign using an all-terrain vehicle. Tests on real data show high classification performance for road scenarios and rural environments; the generality of the method makes it applicable for different types of mobile robots including, but not limited to, autonomous vehicles.

Author

Mauro Bellone

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Giulio Reina

Universita del Salento

Luca Caltagirone

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Mattias Wahde

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN)

Vol. 4 1

Areas of Advance

Transport

Subject Categories

Robotics

DOI

10.1109/TITS.2017.2769218

More information

Latest update

3/19/2019