of Subarctic Forests Philippe Babin, Philippe Dandurand, Vladimr - - PowerPoint PPT Presentation

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of Subarctic Forests Philippe Babin, Philippe Dandurand, Vladimr - - PowerPoint PPT Presentation

Jun 16, 2023 383 likes •715 views

12th FSR conference, Tokyo, 2019 Large-scale 3D Mapping of Subarctic Forests Philippe Babin, Philippe Dandurand, Vladimr Kubelka, Philippe Gigure and Franois Pomerleau Subarctic Boreal Forest: Research Opportunity 2/27 Naive approach

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Large-scale 3D Mapping

  • f Subarctic Forests

Philippe Babin, Philippe Dandurand, Vladimír Kubelka, Philippe Giguère and François Pomerleau

12th FSR conference, Tokyo, 2019

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Subarctic Boreal Forest: Research Opportunity

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Our approach Naive approach

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Applications

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Local Wildlife Snow Fall Path obstacles Uneven Path

Challenge of Field Tests

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Mapping of Subarctic Boreal Forest - Challenges

 Unstructured environment → hard to map  Cold temperatures → noisy sensor  Few visual features due to snow → bad for vision based approaches

6/27

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Related Work

Williams et al., 2009

Paton et al., 2016

7/27

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Contributions

 Large-scale mapping of difficult environments  Novel fusion of IMU and GNSS measurement

inside of ICP

 Generated maps are crisp and without long term

drifts

 Introduced optimization to scale to large map 8/27

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Dataset Environment

4.1 km of forest path

9/27

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10/27

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Data Acquisition Platform

GNSS station (RTK)

RS-16 lidar

MTI-30 IMU

10h of battery life

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Iterative Closest Point (ICP)

T ?

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ICP

Tinit T

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Iterative Closest Point (ICP)

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Iterative Closest Point (ICP)

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Sensor Fusion

Lidar ICP IMU GNSS SLAM Lidar Penalty-ICP IMU GNSS map pose Classical approach Our approach map pose

Covariance [1] pose [1] D. Landry, F. Pomerleau, and P. Giguère. CELLO-3D: Estimating the Covariance of ICP in the Real World. In ICRA, 2019 15/27 Point cloud Pose Covariance Pose/Covariance Legend

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Prior ICP with penalty ICP no penalty

Map of lake Dataset

350m

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ICP with penalty ICP no penalty

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Crispiness locally consistent Prior ICP With penalties ICP Without Penalties

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Prior ICP with penalty ICP no penalty

Map of forest Dataset

500m

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Prior ICP with penalty ICP no penalty

Map of forest Dataset

500m

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Map of skidoo Dataset

ICP no penalty ICP with penalty Prior 670m

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ICP no penalty ICP with penalty Prior 670m

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Map of skidoo Dataset

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Full Map with Penalty

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Performance improvements

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Future work

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Future work – Project SNOW

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Questions?

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Performance improvements

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Results: effect of penalties

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Penalty-ICP

  • Leverage ICP’s minimizer for sensor fusion
  • Add penalty term based on GNSS and IMU estimate
  • Introduced a point to Gaussian cost function
  • Minimize Mahalanobis distance instead of the Euclidian distance

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Full Map

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