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.
Learning Traversability From Point Clouds in Challenging Scenarios
IEEE Transactions on Intelligent Transportation Systems ; 19 , 1 ; 296-305
2018-01-01
3902125 byte
Article (Journal)
Electronic Resource
English
Learning traversability models for autonomous mobile vehicles
British Library Online Contents | 2008
|