4.7 Article

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

期刊

REMOTE SENSING
卷 12, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/rs12111870

关键词

robotics; localization; delaunay triangulation; SLAM; forest localization

资金

  1. Business Finland [26004155]
  2. Academy of Finland [328755]
  3. Academy of Finland (AKA) [328755, 328755] Funding Source: Academy of Finland (AKA)

向作者/读者索取更多资源

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 point clouds are matched to a global 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 200 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 are realistic during forest operations.

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