Bio-inspired Vision-based Frontal Obstacle

Avoidance for UAVs in An Unstructured

Environment

Ashutosh Natraj

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Dr. Ashutosh Natraj, MIEEE

University of Oxford, Oxford, UK

CogArch, March 13th, 2015, Istanbul, Turkey

Content

Introduction

Motivation

Proposed Solution

Experimental Results

Conclusion

Ashutosh Natraj, MIEEE

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Introduction:

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Autonomous Intelligent Systems

Program(AISP) Consortium

Introduction:

Types of Robots:

Unmanned Ground Vehicle (UGV) Autonomous underwater Vehicle (AUV)

Unmanned Aerial Vehicle (UAV)

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UAVs and AUVs aremuch more challengingdue to its degree offreedom with acapability to reachabsolutely all positions.

Why Interest in Obstacle Detection

and Avoidance? For robots to be autonomous and intelligent require them to

know about their environment?

Lets help build robots that can perceive the environment!

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But How

State Estimation (Pose)

Obstacle Detection & Avoidance

Communicate with the environment

Obstacle Detection & Avoidance:

State of the art: Types of Sensor Obstacle Detection & Avoidance ?:

Range Finding Devices Proposed by Wurm et al. [9]

& Merz et al. [10]

Lidar

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Laser Range Finder

However not suitable:

Heavy, Required Data fusion,

Computationally expensive and

consumes more battery power

Note: Please refer to reference from the paper

Obstacle Detection & Avoidance:

Type of Sensor Obstacle Detection & Avoidance ?:

Stereo Devices Proposed by

Bachrach et al. [11] use of

Kinnect

Hrabar et al. [6] and Na et

al. [7] use of stereo vision

Stereo Vision System

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Kinnect

However not suitable:

Heavy, Required Data fusion,

Computationally expensive and

consumes more battery power

Note: Please refer the reference from the paper

Obstacle Detection & Avoidance:

Type of Sensor Obstacle Detection & Avoidance ?:

Monocular Vision

Monocular Vision System

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Most suitable:

Light in weight, Computationally

less expensive and consumes less

battery power, Doesn’t require

GPS coordinates.

Monocular Vision is difficult, but

WHY?

Motivation:

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Birds nod head! BUT WHY?

Audience Experiment:

Experiment 2: Close one eye, try to tell me

which object is near and which is far?

Experiment 1: Close one eye, try to touch

the finger tips !

Monocular Vision-based Obstacle

Detection & Avoidance

Optical Flow (OF)

Structures From Motion (SFM)

Monocular Vision-based Obstacle Detection & Avoidance

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Motion Parallax generated from the UAV

Inefficient in detecting frontal obstacles & required motion fields for depth

perception

Lines from the environment

Inefficient upon entering large halls/rooms.

Vanishing Line Technique

Monocular Vision-based Obstacle

Detection & Avoidance

Optical micro sensors and Neural Nets for Control Gain as by

Oh et al.[14]

Multi scale oriented Patches (MOPS)as by Lee et al. [20]

Time To Collision (TTC) as by N Lee et al. [21]

Monocular Vision-based Obstacle Detection & Avoidance

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Computationally expensive and lacked depth perception to

trigger quick UAV control commands

Lack of information on exact distance values:

Note: Please refer to reference from the paper

Proposed Solution:

Our Approach:Bio-inspired Solution from the nature:

How Bees/flies detect and avoid obstacles.

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View from

Bee’s eye:

Proposed Solution:

Compute position of obstacle in 3D Space from subsequent captured images.

Obtain position of the UAV’s camera in 3D space from the IMU.

Compute the Euclidean distance between the two

Our Approach:Estimate relative distance of the Obstacle from UAV’s camera.

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Mathematical Modeling:

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Coordinate Frames:

{C},{R} & {W} respectively for

Camera, Robot and World.

P0: Obstacle in 3D space.

PI : Projection of obstacle on

Image

: Unit Vector Orthogonal to

Image Plane

Mathematical Modeling: Algorithm

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Algorithm:

Results:

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Sorry! Can not show

other results at this

stage.

Results: Obstacle detection & avoidance on board UAV

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Conclusion:

Frontal Obstacle was detected and avoided

Is computationally less expensive than most other solutions.

Estimation in GPS deficient & unstructured environment

achieved.

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Ashutosh Natraj

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Thank you