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Asian Institute of Technology
Indoor SLAM Monocular SLAM with indoor surface estimation and visualization Where are you? Umm not sure.. Thesis by: Ran Zask Asian Institute of Technology
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Agenda Background Problem statement Related work Our algorithm Results
3D modeling Texture mapping Results Problems & limitations Conclusions & Recommendations
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Background Some robot applications require human operator (e.g. search & rescue) Human operator needs to maintain situation awareness in order to control the robot. Current means of maintaining situation awareness are poor (2D maps, live video) 3D modeling and visualization is needed.
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Problem statement Real-time SLAM implementations do not estimate surfaces of the environment and using expensive sensors (sonar, laser) Structure From Motion is too heavy and done offline. Real-time 3D modeling and visualization is feasible and needed.
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Related work Pollefeys et al. (2004) : point-based modeling
Monocular camera 3D reconstruction from feature points Bundle adjustment Rectification, Dense stereo Offline algorithm, Massive computation Similar application: Microsoft Photosynth
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Related work (cont.) Iterative closest point (ICP) Mesh registration
Johnson and Kang (1999) : grid-based modeling Omni-directional stereo 2D Delauney triangulation Iterative closest point (ICP) Mesh registration Massive computation
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The algorithm Single camera, calibrated Requires SLAM (point-based)
Uses 3D points and occupancy grids Online algorithm Creates 3D models with texture mapping Provides low metric accuracy Less noise between grid cells
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The algorithm Uses 2 occupancy grids: Local grid Global grid
Recreated each frame Contains ‘fullness’ seen at the current frame Incrementally added to the global grid. Global grid Contains the incremental ‘fullness’ Used for Iso-surface calculation
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The algorithm (3D modeling)
For each image: Input: new 3D point set (from SLAM) Reset local grid Associate 3D points with cells in the local grid Ignore cells associated by few points Add camera-center as an occupied cell Fill-in the grid (convex-hull) Merge local grid into global grid Iso-surface the global grid -> last model
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The algorithm (texture mapping)
For each image : (After having the latest model) Classify triangles as: “new,” “old,” and “expired” Project “new” triangles onto current frame -> textmaps Remove “expired” triangles
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Results Modeling accuracy is satisfying Texture mapping
Tessellation within a patch: perfect Tessellation between patches: Inaccurate The more images are closer to each other, the better the modeling and the texture mapping “patch” – collection of several triangles of the same image
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Modeling results
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Modeling results
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Modeling results
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Summary We created an online algorithm for modeling with texture, all from a single camera. Low computation is required per image This should allow short baseline -> good results Real time implementation of the system is feasible. Why it’s a useful aproach
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Future work Create a real-time system
Create 3D tools for the human operator to easily ‘walk’ in the model and navigate the robot. Improving the algorithm even more: performing iso-surface on tighter grids Why it’s a useful aproach
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DEMO
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