Augmenting traversability maps with ultra-wideband radar to enhance obstacle detection in vegetated environments

Abstract

Operating in vegetated environments is a major challenge for autonomous robots. Obstacle detection based only on geometric features causes the robot to consider foliage, for example, small grass tussocks that could be easily driven through, as obstacles. Classifying vegetation does not solve this problem since there might be an obstacle hidden behind the vegetation. In addition, dense vegetation typically needs to be considered as an obstacle. This paper addresses this problem by augmenting probabilistic traversability map constructed from laser data with ultra-wideband radar measurements. An adaptive detection threshold and a probabilistic sensor model are developed to convert the radar data to occupancy probabilities. The resulting map captures the fine resolution of the laser map but clears areas from the traversability map that are induced by obstacle-free foliage. Experimental results validate that this method is able to improve the accuracy of traversability maps in vegetated environments.

Normal Distributions Transform Traversability Maps: LIDAR‐Only Approach for Traversability Mapping in Outdoor Environments

“…Unfortunately, it is not always enough for safe navigation to be able to distinguish vegetation from other obstacles, because there might be an obstacle hidden behind the vegetation. This problem was addressed in a recent study, in which UWB radar was used in parallel with LIDAR to augment traversability maps in vegetated environments (Ahtiainen et al., , ). It was shown that because the utilized UWB radars are capable of penetrating around of vegetation, it is possible to generate accurate traversability maps of densely vegetated environments such that solid obstacles hidden behind dense vegetation can still be detected.…”

“…This constraint may be relaxed in future works by fusing our traversability results with classifications based on the algorithms proposed in Ahtiainen et al. () and Ahtiainen et al. ().…”

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

“…A recent and very complete survey on the terrain traversability analysis for autonomous ground vehicles is presented in [212]. The authors define a taxonomy for the different methods by grouping them into vision-based (e.g., [213,214]), LiDAR-based (e.g., [215,216]), alternative sensor-based (e.g., [217][218][219]), and sensor fusion methods (e.g., [220,221]).…”