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Large-scale 3D Mapping of Subarctic Forests

EasyChair Preprint no. 1350

14 pagesDate: July 30, 2019


The ability to map challenging subarctic environments opens new horizons for robotic deployments in industries such as forestry, surveillance, and open-pit mining. In this paper, we explore possibilities of large-scale lidar mapping in a boreal forest. Computational and sensory requirements with regards to contemporary hardware are considered as well. The lidar mapping is often based on the SLAM technique relying on pose graph optimization, which fuses the Iterative Closest Point (ICP) algorithm, Global Navigation Satellite System (GNSS) positioning, and Inertial Measurement Unit (IMU) measurements. To handle those sensors directly within the ICP minimization process, we propose an alternative approach of embedding external constraints. Furthermore, a novel formulation of a cost function is presented and cast into the problem of handling uncertainties from GNSS and lidar points. To test our approach, we acquired a large-scale dataset in the Foret Montmorency research forest. We report on the technical problems faced during our winter deployments aiming at building 3D maps using our new cost function. Those maps demonstrate both global and local consistency over 4.1km.

Keyphrases: anisotropic, cost function, forest, GNSS, GPS, ICP, LiDAR, Mapping, penalties, Winter

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Philippe Babin and Philippe Dandurand and Vladimír Kubelka and Philippe Giguère and François Pomerleau},
  title = {Large-scale 3D Mapping of Subarctic Forests},
  howpublished = {EasyChair Preprint no. 1350},

  year = {EasyChair, 2019}}
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