Indoor SLAM Monocular SLAM with indoor surface estimation and visualization

Indoor SLAMMonocular SLAM with indoor surface estimation and visualization

Thesis by:

Ran Zask 105221

Asian Institute of Technology

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Whereare you?

Agenda

Background

Problem statement

Related work

Our algorithm

o3D modeling

oTexture mapping

Results

Problems & limitations

Conclusions & Recommendations

Background

Some robot applications require human operator (e.g. search &rescue)

Human operator needs to maintain situation awareness in orderto control the robot.

Current means of maintaining situation awareness are poor (2Dmaps, live video)

3D modeling and visualization is needed.

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Problem statement

Real-time SLAM implementations do not estimatesurfaces of the environment and using expensivesensors (sonar, laser)

Structure From Motion is too heavy and done offline.

Real-time 3D modeling and visualization is feasibleand needed.

Related work

Pollefeys et al. (2004) : point-based modeling

oMonocular camera

o3D reconstruction from feature points

oBundle adjustment

oRectification, Dense stereo

Offline algorithm, Massive computation

Similar application: Microsoft Photosynth

Related work (cont.)

Johnson and Kang (1999) : grid-based modeling

oOmni-directional stereo

o2D Delauney triangulation

oIterative closest point (ICP)

oMesh registration

Massive computation

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

The algorithm

Uses 2 occupancy grids:

Local grid

oRecreated each frame

oContains ‘fullness’ seen at the current frame

oIncrementally added to the global grid.

Global grid

oContains the incremental ‘fullness’

oUsed for Iso-surface calculation

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

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

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 themodeling and the texture mapping

“patch” – collection of several triangles of the same image

Modeling results

Modeling results

Modeling results

Summary

We created an online algorithm for modeling withtexture, 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.

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