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Precise localisation and Mapping in forest with6DOF SLAM

Marek Pierzcha la1 Philippe Giguere2 Rasmus Astrup1

1Norwegian Institute for Bioeconomy ResearchAas, Norway

2Department of Computer Science and Software EngineeringLaval University, Quebec, Canada

Precision Forestry Symposium, Stellenbosch, 2017

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Outline

Aims

Introduction

MethodsMappingExtraction of stand properties from map

Results

Conclusions

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Aims

Overall goal is to test graph-SLAM for mapping of a forestedenvironment using a 3D LiDAR-equipped UGV.

1. Test the feasibility of creating a 3D map of the ground surfaceand lower stems based on data captured by UGV.

2. Quantify the accuracy of the extracted diameters anddistances between trees.

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Introduction

Simultaneous localization and mapping (SLAM) is thecomputational problem of constructing or updating a map of anunknown environment while simultaneously keeping track of anagent’s location within it.

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Aims

Introduction

MethodsMappingExtraction of stand properties from map

Results

Conclusions

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Mapping

Field test was carried out in southern Norway. We mapped themature (harvest class 5)spruce s stand with no understory. Wecovered area of about 900m2.

Figure: The mobile platform used for data collection (to the left) GRAPHSLAM concept (to the right).

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Introduction

MethodsMappingExtraction of stand properties from map

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Extraction of stand properties from map1. Segmentation (extracting ground points)2. Computing tree heights3. Extracting diameters4. 2D projection and clustering (DBscan)5. Circle fitting

Figure: Support ratio S . On the fitted circle, the green points indicatethe proportion of stem circumference that is supported by inliers (redpoints)and the outliers (blue points)

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VIDEO

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Conclusions

1. Succesful generation of map and accurate pose estimation

2. DBH estimations performed similarly to other studies.Relationship between accuracy and the value of support ratiowas shown.

3. The study revealed potential research opportunities on SLAMin forest environment.