A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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




Julkaisun tekijät: QingQing Li, Paavo Nevalainen,Jorge Peña Queralta,Jukka Heikkonen, Tomi Westerlund

Kustantaja: MDPI

Julkaisuvuosi: 2020

Journal: Remote Sensing

Volyymi: 12

Julkaisunumero: 11

Sivujen määrä: 22

eISSN: 2072-4292

DOI: http://dx.doi.org/10.3390/rs12111870

Verkko-osoite: https://www.mdpi.com/2072-4292/12/11/1870


Tiivistelmä

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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Last updated on 2021-24-06 at 09:52