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

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

Blog Article

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 Plates 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 Fireplace Insert with Crushed Glass data processing for speeds not exceeding 0.5 m/s.

The accuracy and speed limit are realistic during forest operations.