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.

Impact and interest:

1 citations in Scopus
1 citations in Web of Science®
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ID Code: 86221
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Mobile robots, Obstacle Detection, Terrain Traversability Analysis, ultra wideband radar, LIDAR
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 IEEE
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Deposited On: 05 Aug 2015 22:24
Last Modified: 26 Jun 2017 16:34

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