Terrain Classification Based On Structure ForAutonomous Navigation in Complex Environments

Duong V.Nguyen1, Lars Kuhnert2, Markus Ax2, and Klaus-Dieter Kuhnert2

1Research School MOSES, University of Siegen, Germany

2Institute for Real-Time-Learning Systems, University of Siegen, Germany

II. Signal Processing And Application

Introduction

Methodology• Graph-Cut

• Feature Extraction

• Neighbor Distance Variation Inside Edgeless Areas

• Conditional Local Point Statistics

• Support Vector Machine

Experiments and Results

Conclusion

Reference

Outline

Introduction

•Variety of terrain •Avoid obstacles• Maintain rollover stability• Manage power …etc

Why do we need Terrain Classification?

autonomous operation Or: complete task without direct control by a human• Bomb-defusing • Vacuum cleaning • Forest exploration …etc

What is unmanned system ?

AMOR:

1st prize of innovation awards, ELROB-2010, Hammelburg, Germany.

PMD camera Laser Scanner Stereo Cameras

Introduction Recent 3-D Approaches

Problems: Beam scattering effects Only used for static scenes Object detection purely based on structure is

not really robust in some scenes.

Solutions: Local points statistic analysis (Graph-Cut for depth image segmentation) Gaussian Mixture Model using Expectation

maximization Combining 3-D and 2-D features

Why should Laser Scanner be used?

Advantages: Stable data acquisition High precision Affordable

Introduction

Classifier SVM

ROI extraction

3-D point cloud

3-D Features Depth image segmentation

Methodology

Terrain Classification System Diagram

Graph-Cut Technique

Methodology

Internal difference

Component difference

Un-Joint Condition:

Classifier SVM

ROI extraction

3-D point cloud

3-D Features Depth image segmentation

Methodology Feature Extraction

Classifier SVM

ROI extraction

3-D point cloud

3-D Features Depth image segmentation

Methodology Support Vector Machine

Experiments and Results

• Graph-cut Technique For Segmentation• Neighbor Distance Variation Feature• Conditional Local Point Statistics Feature

Future work:• 2D&3D Calibration• Color Features

Conclusion

Q&A

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