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Large-scale 3D Mapping of Subarctic Forests

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

12th FSR conference, Tokyo, 2019

Subarctic Boreal Forest: Research Opportunity

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Our approachNaive approach

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Applications

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Local WildlifeSnow 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

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Related Work

Williams et al., 2009 Paton et al., 2016

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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

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Dataset Environment

4.1 km of forest path

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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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ICPTinit

T

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Iterative Closest Point (ICP)

Iterative Closest Point (ICP)

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Sensor Fusion

Lidar ICP

IMU

GNSS

SLAM

Lidar

Penalty-ICPIMU

GNSS

mappose

Classical approach Our approach

mappose

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

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Point cloud

Pose

Covariance

Pose/Covariance

Legend

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 penaltyICP with penaltyPrior

670m

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ICP no penaltyICP with penaltyPrior

670m

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Map of skidoo Dataset

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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