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arXiv · 2005.05662

Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

Qingqing Li·Paavo Nevalainen·Jorge Peña Queralta·Jukka Heikkonen·Tomi Westerlund
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Abstract

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local observations are matched to a general tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 2100\,m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12\,cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and speed limit is realistic during forest operations.

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Qingqing Li, Paavo Nevalainen, Jorge Peña Queralta, Jukka Heikkonen, Tomi Westerlund. 2020-05-12. Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation. https://doi.org/10.3390/rs12111870

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