Learned ultra-wideband RADAR sensor model for augmented LIDAR-based traversability mapping in vegetated environments
Ahtiainen, Juhana, Peynot, Thierry, Saarinen, Jari, Scheding, Steven, & Visala, Arto (2015) Learned ultra-wideband RADAR sensor model for augmented LIDAR-based traversability mapping in vegetated environments. In Proceedings of the 18th International Conference on Information Fusion, IEEE, Washington, DC, pp. 953-960.
In vegetated environments, reliable obstacle detection remains a challenge for state-of-the-art methods, which are usually based on geometrical representations of the environment built from LIDAR and/or visual data. In many cases, in practice field robots could safely traverse through vegetation, thereby avoiding costly detours.
However, it is often mistakenly interpreted as an obstacle. Classifying vegetation is insufficient since there might be an obstacle hidden behind or within it.
Some Ultra-wide band (UWB) radars can penetrate through vegetation to help distinguish actual obstacles from obstacle-free vegetation. However, these sensors provide noisy and low-accuracy data. Therefore, in this work we address the problem of reliable traversability estimation in vegetation by augmenting LIDAR-based traversability mapping with UWB radar data.
A sensor model is learned from experimental data using a support vector machine to convert the radar data into occupancy probabilities. These are then fused with LIDAR-based traversability data. The resulting augmented traversability maps capture the fine resolution of LIDAR-based maps but clear safely traversable foliage from being interpreted as obstacle.
We validate the approach experimentally using sensors mounted on two different mobile robots, navigating in two different environments.
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|Item Type:||Conference Paper|
|Keywords:||Mobile robots, Obstacle Detection, Terrain Traversability Analysis, ultra wideband radar, LIDAR|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 IEEE|
|Copyright Statement:||Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.|
|Deposited On:||05 Aug 2015 22:24|
|Last Modified:||26 Oct 2015 01:24|
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