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Article

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

Turku Intelligent Embedded and Robotic Systems, University of Turku, 20500 Turku, Finland
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Remote Sens. 2020, 12(11), 1870; https://doi.org/10.3390/rs12111870
Received: 7 May 2020 / Revised: 31 May 2020 / Accepted: 5 June 2020 / Published: 9 June 2020
(This article belongs to the Section Forest Remote Sensing)
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. View Full-Text
Keywords: robotics; localization; delaunay triangulation; SLAM; forest localization robotics; localization; delaunay triangulation; SLAM; forest localization
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MDPI and ACS Style

Li, Q.; Nevalainen, P.; Peña Queralta, J.; Heikkonen, J.; Westerlund, T. Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation. Remote Sens. 2020, 12, 1870. https://doi.org/10.3390/rs12111870

AMA Style

Li Q, Nevalainen P, Peña Queralta J, Heikkonen J, Westerlund T. Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation. Remote Sensing. 2020; 12(11):1870. https://doi.org/10.3390/rs12111870

Chicago/Turabian Style

Li, Qingqing, Paavo Nevalainen, Jorge Peña Queralta, Jukka Heikkonen, and Tomi Westerlund. 2020. "Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation" Remote Sensing 12, no. 11: 1870. https://doi.org/10.3390/rs12111870

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