(Reading Group) First Results in Detecting and Avoiding Frontal Obstacles from a Monocular Camera for Micro Unmanned Aerial Vehicles

First Results in Detecting and Avoiding Frontal Obstacles

from a Monocular Camera for Micro Unmanned Aerial

Vehicles

WakaWaka Group : H.Kidane , I.Sadek , and

M.Elawady

8/18/2014 1

Supervisor : Prof. Yvan Petillot

Robot Project

Tomoyuki Mori and Sebastian Scherer

Outlines

• Introduction

• Related Work

• Approach–Detection

–Avoidance

• Experiments

• Conclusion

• Future Work

8/18/2014 B31XP Robotics Project 2

Outlines

• Introduction

• Related Work


–Avoidance

• Experiments

• Conclusion

• Future Work


Introduction

• Limited payload to carry

additional sensors

• Depended on monocular

camera

• Obstacles can’t be observed

directly using this camera


Problem Definition

Introduction


Objective

Detecting and avoiding frontal obstacles by utilizing the size change

Outlines

• Introduction

• Related Work


–Avoidance

• Experiments

• Conclusion

• Future Work


Related Work


Motion Parallax

Monocular Cues

Stereovision

Related Work


Motion Parallax

Monocular Cues

Stereovision

Related WorkMotion Parallax


Near objects move very quickly. However , distant objects move much more slowly

http://www.garyfisk.com/anim/mparallax.swf

Motion Availability

Optical Flow/Structure from Motion- Large Computation.

- Cannot detect obstacles straight a head

http://www.garyfisk.com/anim/mparallax.swf

Related Work


Motion Parallax

Monocular Cues

Stereovision

Related Work


Monocular Cues

Monocular Cues provide depth information when viewing a scene with one eye (Wiki )

Method Availability

Perspective- Long lines detection

- Well strutted environments (In door)

Relative Size

- Roughly similar objects (The larger the

object the closer to the observer)

- Available for frontal obstacle avoidance

Known Object Size- Features are required for particular objects

Texture Gradient

Depth From Focus - Not applicable for small aperture cameras

Related Work


Motion Parallax

Monocular Cues

Stereovision

Related Work


StereovisionStereovision is to extract 3D information from digital images (Wiki )

Method Availability

Stereoscopic

parallax- Needs a sufficient baseline

Convergence

-The convergence is the angle formed by your

eyes and the observed object

- Available only for human

Outlines

• Introduction

• Related Work


–Avoidance

• Experiments

• Conclusion

• Future Work



ApproachFrontal Obstacle Detection

Depth

Relative

Size

Natural Environment



After Few Frames

Detect “Relative Size”



Algorithm 1

Scale Expansion

Detector

Current

Image

Previous

Image

Algorithm 2

Template

Matching

Position of

Obstacle



Algorithm 1 : Scale Expansion DetectorSURF/SIFTComputation

Scale

Changes



Algorithm 2 : Template Matching

SURF Scale VS

Correlation Distance


• Karaman and Frazzoli

– Used position distribution to model the tree location

• For this kind of forest only the closest obstacle is really relevant

This motivate use of reactive obstacle avoidance law

ApproachReactive Obstacle Avoidance

for Forest Flight


J. J. Gibson : animals detect “visual collision” with looming and do not detect the distance

Likewise the vehicle avoids frontal obstacle when it detects that the time to collision is too small


for Forest Flight



for Forest Flight

Frew et al: proposed a trajectory generator for a small

UAV to fly in forest

But as the Ar.Drone is small and it has to react quickly,

path planning is not used.



for Forest Flight

• Information the Arial vehicle has to know:• Bearing of the closet tree

• Goal position

• It’s own position

• Control command to the UAV:• velocity

• Approach of control command:• Fly sideways when obstacle found in field of view

• otherwise control yaw angle to achieve the goal bearing



for Forest Flight



for Forest Flight

Parameters required to determine feasibility of flight:

• Response time

• Acceleration limit

• Flying speed

• Obstacle sensing distance

• Field of view

• Trunk size

• Minimum distance between trees

Outlines

• Introduction

• Related Work


–Avoidance

• Experiments

• Conclusion

• Future Work



Experiment setup

• Parrot Ar.Drone • Simulation software

• Intel [email protected]

running Linux (Laptop)

• Frontal Camera (FOV 92°, Res. 320x240, 10Hz)

• Sonar height sensor, Inertial measurement unit, down camera


Experiment


Experiment Result

• Failures are due to slow response time

Outlines

• Introduction

• Related Work


–Avoidance

• Experiments

• Conclusion

• Future Work



Conclusion

• They develop scale expansion detector to detect the approaching object using monocular vision

• They use SURF features to extract the expanding key points

• Obstacles have to have sufficient texture to make SURF key points

• The performance of this approach can be improved using better vehicle platform which has fast response time

Outlines

• Introduction

• Related Work


–Avoidance

• Experiments

• Conclusion

• Future Work



Future Work

Combining multiple detection algorithms:

– Optical flow

– Perspective cues

Which are effective in:

– Textured natural environments

– Homogeneous urban environments

Engineering

(Reading Group) First Results in Detecting and Avoiding Frontal Obstacles from a Monocular Camera for Micro Unmanned Aerial Vehicles