Detecting and Avoiding Frontal Obstacles from a Monocular

Camera for Micro Unmanned Aerial Vehicles

1Robot Project

WakaWaka Group

Professor: Yvan Petillot

Team Members : H.Kidane , I.Sadek , M.Elawady

Heriot Watt University

School of Electrical and Physical Sciences

Outline

• Introduction

• Related work

• Methodology–Detection

–Avoidance

• Experiments

• Conclusion

• Future Work

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Outline

• Introduction

• Related work

• Methodology–Detection

–Avoidance

• Experiments

• Conclusion

• Future Work

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Introduction

GoalDetecting and avoiding frontal obstacles using Ar-Drone2

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Introduction

Ar.Drone It is a rotating rigid structure with 6 degree of freedom. The two pair of rotors

rotate in different directions

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HD Camera 720P , 30FPSVery Light and High

Resistance Plastic

Specific

Propeller

Ultrasound Sensor

Indoor Weight :420g

Price : $300

• Applications:

MAVs play an important role in many applications (i.e.

search, monitoring, rescue, surveillance, etc)

- Able to maneuver rapidly and adequately.

- less dangerous for people.

- Provide real time data to the operator.

• Limitation:

- Limited payload to carry additional sensors.

- Depend on monocular camera.

- Obstacles can’t be observed directly using this camera.

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Introduction

Outline

• Introduction

• Related Work

• Methodology–Detection

–Avoidance

• Experiments

• Conclusion

• Future Work

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Related Work

• Paper1: Learning Monocular Reactive UAV Control in Cluttered Natural EnvironmentsStephane Ross, Narek Melik-Barkhudarov, Kumar Shaurya , Shankar Andreas Wendel, DebadeeptaDey, J. Andrew Bagnell, Martial Hebert

• Paper2: First Results in Detecting and Avoiding Frontal Obstacles from a Monocular Camera for Micro Unmanned

Aerial VehiclesTomoyuki Mori , Sebastian Scherer

• Paper3: Autonomous quad rotor flight with vision-based obstacle avoidance in virtual environmentAydın Eresen, Nevrez Imamoglu, Mehmet Önder Efe

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Related Work-Paper1

The system observes a human expert driving the drone

Video Stream

Visual Features

Expert

Input

Unsupervised Learner

Control Command

http://robotwhisperer.org/bird-muri/

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Related Work-Paper1

Example shows the learning process where learner in this frame

gives wrong results (white line), while the expert provides the correct command

(red line).

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Related Work-Paper2

This method relies on the relative size change of an object in two

consecutive frames

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Related Work-Paper2

Position of obstacle

Confirm scale with template matching

Discard key-points (smaller or same size)

Discard mismatch (Distance threshold)

Matching in consecutive frames

Generate surf key points

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Related Work-Paper3

Take Snap Shot

Image Pre-processingGoal

Achieved

Object Detection

Generate PathYaw Angle

Landing

Controller

No

Yes

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Related Work-Paper3

• Image pre-processing: resizing and de-blurring

• Object detection: optical flow (Horn and Schunk)

Search

WindowTemplate

Google earth environment

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Related Work-Paper3

Result

Outline

• Introduction

• Related work

• Methodology–Detection

–Avoidance

• Experiments

• Conclusion

• Future Work

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Methodology – Detection

Un-successful Works

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Methodology – Detection

Semi-Dense Optical Flow

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Outline

Check image de-blurring (variance of laplacian)

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BlurredNot Blurred

Correct Incorrect

Not Blurred (7 Images) 6 1

Blurred (8 Images) 8 0

https://www.mathworks.co.uk/matlabcentral/fileexchange/27314-focus-

measure/content/fmeasure/fmeasure.m

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Methodology – DetectionBlock Diagram

Level 1

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Image at

frame x

[1]

Image at

frame x+k

[2]

(Optional)

Pre-processing

image resizing

and sharpening

[1] Compute

symmetric

feature locations

within step range

Optical-flow

Algorithm

[Gunnar

Farneback]

Mismatch points

removal

[euclidean distance]

(Optional)

[2] Region of interest

(ROI) column selection

[25%]

[2] Split image into

five regions

[FL,NL,CN,NR,FR]

Calculate

average/median

euclidean distance

for each region

Find region with

maximum value

last 5 max ==

current max

&&

current max

>= threshold

yesObstacle

direction

[left/right]

No

No

obstacle

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Methodology – DetectionBlock Diagram

Level 0

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Image at

frame x

Image at

frame x+k Obstacle

direction

[left/right]

No obstacle

Detection

Algorithm OR

Outline

• Introduction

• Related work

• Methodology–Detection

–Avoidance

• Experiments

• Conclusion

• Future Work

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Takeoff

Fly Forward

Detected?

Process

video to detect obstacle

Fly sideways

Land/wait_joy_cmDestination/

joy_active?

Yes

Yes

No

No

Avoidance

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ROS driver for Parrot AR.Drone

Avoidance

• "ardrone_autonomy” developed in Autonomy Lab of Simon Fraser University

Avoidance

• Information from Ar.Drone will be published in ardrone/navdatatopic

• ardrone/navdata

– Battery percent

– Drone state

– Orientations/tilt magnitudes

– pressure

– etc

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Receiving data from AR.Drone

”ardrone_anatomy“

Avoidance

• ROS camera interface topics to

capture Images/video from Drone

– ardrone/image_raw

– ardrone/front/image_raw

– ardrone/bottom/image_raw

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Receiving data from AR.Drone

”ardrone_anatomy“

Avoidance

• Drone will takeoff, land, or

emergency stop/reset by

publishing an Empty ROS

messages to the ff topics

– ardrone/ takeoff

– ardrone/land

– ardrone/reset

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Sending commands to AR.Drone

Avoidance

• To fly the Drone after takeoff,

publish a message of type

geometry_msgs::Twist to the

cmd_vel

• geometry_msgs::Twist expresses

velocity in free space broken into its

Linear and angular

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Sending commands to AR.Drone

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Waka_Controller ardrone_driver

/cmd_vel

/ardrone/takeoff

/ardrone/land

/ardrone/reset

/ardrone/front/image_rawWaka_Image_Proce

Avoidance

Autonomous flying controller

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Avoidance

Integrating with Joystick

/joy

Waka_Controller ardrone_driver

/cmd_vel

/ardrone/takeoff

/ardrone/land

/ardrone/reset

/ardrone/front/image_rawWaka_Image_Proce

Joy_node

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Sideway velocity for 1s

Avoidance

geometry_msgs::Twist /cmd_vel

Forward Velocity

linear.x: 1m/s (move forward)

linear.y: 0

linear.z: 0

Obstacle in half left

linear.x: 0

linear.y: -2/s move right

linear.z: 0

Obstacle in half right

linear.x: 0

linear.y: 2m/s move left

linear.z: 0

Outline

• Introduction

• Related work

• Methodology–Detection

–Avoidance

• Experiments

• Conclusion

• Future Work

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• Setup:

- Intel Core™ i7-3630 QM Processor

- Clock speed : 2.40 / 3.40 Turbo GHz

- 3rd level cache : 6 MB

- Running OS: Linux (Ubuntu)

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Experiments

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Experiments

Control training

virtual obstacle

Online Obstacle

Avoidance test

Detection training

using offline video

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Offline Online

Correct Incorrect Total Correct Incorrect Total

Indoor 7 5 12 7 3 10

Outdoor 8 4 12 - - -

Experiments

Results

Outline

• Introduction

• Related work

• Methodology–Detection

–Avoidance

• Experiments

• Conclusion

• Future Work

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• Optical flow algorithm gives better detection results

comparing with feature-based algorithms

• Control part for avoidance reacts as expected

• Accuracy is reduced due to inaccurate measurement

of time to collusion

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Conclusion

Outline

• Introduction

• Related work

• Methodology–Detection

–Avoidance

• Experiments

• Conclusion

• Future Work

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39Robot Project

Multi-sensor data / multi-detectors for robust time-to-collision estimation

• Frontal camera with Optical flow is used

Optical flow comparisons across all frames

• One comparison at current frame

Find de-blurring kernel for wiener/lucy algorithm

• Neglect blurring images

Path planning and follow m-line to goal

• Fly forward

Future Work

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