Converting point clouds to 3D object maps · Converting point clouds to 3D object maps Part of...
Transcript of Converting point clouds to 3D object maps · Converting point clouds to 3D object maps Part of...
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Converting point clouds to 3D object mapsPart of iTransit Annie Westerlund
2016-05-10 AstaZero Researchers’ day
Agenda
• Short background
• Overview of algorithm
• Description: Point cloud to 3D object map
• Final words
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Background
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Background
Why should we process point clouds?
• ~ 80 000 000 points.
• ~2 GB
• Real-time navigation and
positioning => need
efficient solution.
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Overview of algorithm
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Overview of main algorithm
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XYZRGBI pointcloudInput
Statisticalclassification
Cylinder detector
Road refiner
Wall detector
Lane markingdetector
Object map
Description: Point cloud to 3D object map
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Statistical classification
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• Divide point cloud into XY
grid.
• Fit plane to points in grid.
• Statistical classification:
• Variance XY plane
• Variance Patch plane
• Color
Object detection
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• Wall detection:
• Detect possible ”building clusters”.
• Find vertical planes in clusters.
• Find smallest enclosing cube.
• Cylinder detection:
• Detect poles.
• Find cylinder parameters.
• Lane marking detection:
• Detect lane markings
• Describe lane marking form one or more by cubes.
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• Objects descibed in global coordinates.
• Objects are stored in binary files.
• The map can be visualized in different
layers.
• 9 kB without road surface
• 442 kB with road surface
• Compare 2 GB point cloud
The object Map
AstaZero city and part of rural road
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Describing details
• 3D grid
• Oriented bounding boxes
• Extract main objects first, then describe details
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Final words
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Final words
• Accuracy and resolution
• Positioning – LiDAR and
camera map matching
• Potential for expansion
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Thanks for the attention!