Pázmány Péter Catholic University Faculty of Information Technology and Bionics

See-and-Avoid System for UAV with Five Miniature Cameras

Tamás Zsedrovits†, Ákos Zarándy*†, Zoltán Nagy*†, Bálint Vanek*, András Kiss*†, Máté Németh*†

†Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary,*Computer and Automation Research Institute of the Hungarian Academy of Sciences (MTA SZTAKI)

Budapest, Hungary

EESTEC HIT 01/29/2014

Pázmány Péter Catholic University Faculty of Information Technology and Bionics

Outline• Goals• System requirements• Vision system architecture• Many core processor array implementation• Algorithms

• Unmanned Aerial Vehicle (UAV) technology is maturing• UAVs are ready for autonomous missions technically

• Surveillance tasks • Autonomous missions are not authorized

• Missing redundancy • Missing collision avoidance device

• Automatic collision avoidance device is needed even for remotely piloted UAVs*

Goals

Making way for unmanned aircraftThe FAA’s effort to bring unmanned planes into the nation’s airspace faces tough obstacles, political as well as technical.Work is under way to resolve questions about privacy and to find affordable detect-and-avoid technologies.

Collision avoidance devices• Radar based

• Applied on large airliners (Airbus)

• Radar and vision• Applied on large remotely piloted UAVs (predator)

• Transponder based• TCAS, ADS-B (all manned aircrafts and larger UAVs)

• Vision only • Currently developed (small UAVs)

• Basic requirements• Equivalent safety• Probabilities of collision < 10-11 per flight hour• Layered approach

Requirements• Detect a 10m aircraft from 2km• 0.1 degree/pixel resolution• min. 220x60⁰ view angle• Flyable size/weight/power figures• On-board data storage

intruder

collision volume

separation minima

collision avoidance threshold

traffic avoidance threshold

our track

Framework

Planned on-board sensor and control loop

Architecture• High resolution camera system in the visual range

• Elongated, (220x60⁰)• Large view angle• min 2Mpixel

• FPGA processing system • High computational power• Low power consumption

• Solid state disk• Bandwidth• Capacity• Vibration

Solid State Drive

FPGA board

Camera system

to/from on-board control

220⁰

Camera selection• Single camera with wide angle optics

• Easy from architectural, algorithmic, and processing side• Low distortion, ultra wide view angle optics are bulky

• 3 pieces of C-mount cameras• Good image quality• Relatively large size, volume, and power (1kg, 10W)• High speed serial I/O (USB, Gige, fire-wire) difficult to connect to embedded systems

• 5 pieces of miniature cameras (M12 lens)• Max 1.2 megapixel with global shutter• 50g, 200mW• Poorer image quality

• Micro cameras• Very advantages size/weight/power figures• Rolling shutter only

Sensor and computational system• 5 pieces of wVGA micro cameras

• Aptina MBSV034 sensor• 5g• <150mW• 3.8mm megapixel objectives (M-12)• 70 degrees between two cameras• Total view angle: 220˚x78˚

• FPGA board with Spartan 6 FPGA• Solid State Drive (128Gbyte)

Solid State Drive

FPGA board

752x480 cameras

to/from navigation computer

Mechanics• Stable camera holder

• Alignment• Avoids cross vibration of the cameras• 100g • Aluminum alloy• Electronics in the middle• Covered with aluminum plates

Hardware system• Sensing and processing system

• Field of view: 220°x78°• Resolution: ~2250x752• Frame-rate: 56 FPS• Processor: Spartan6 L45 FPGA • Storage: 128Gbyte (23 min)• Size: 125x145x45mm (5”x6”x1,8”)• Weight: ~450g (1lb)• Power consumption: <8W

Vision system mounted to the airplane

View angle

Panoramic view (stitched images)

Perspective view

Many-core processor arrays implemented in FPGA

• FPGA chips have the largest computational capability nowadays• In affordable medium sized FPGAs:

• Over 200 DSP cores • 200 memory blocks• 500 IO pins• Low power consumption• Special purpose processor arrays

• How to utilize this performance?• Many-core architectures• Specially optimized data and control paths• Distributed control units

Image processing system

Memorycontroller

DRAM

Microblazeprocessor

Image capture

Full framepreprocessing

Gray scaleprocessor

Binaryprocessor

• Input:• DVI/HDMI

• 1920x1080@50Hz• Native camera interface

• Three image processing accelerators

• Parallel operation• Optimized for the image processing

algorithm

Algorithmic components • Aircraft detection against sky background

• Preprocessing on the full frame• Identifying candidate points

• Post processing• Discarding non-relevant candidate points

• Tracking • Multi-level global and local adaptivity

• Aircraft detection against terrain background• Visual-inertial data fusion• Motion based moving object detection

Preprocessing (full frame)• Identifies the candidate aerial

objects• Finds numerous false targets

also• Local adaptation based on

edge density• Global adaptation based on

number of candidate points

contrast calculation

locally adaptive contrast thresholding

candidate points

regions of interest (ROIs)

threshold adjustment

Too many or too few points? y

n

Preprocessing (full frame)• Identifies the candidate aerial

objects• Finds numerous false targets

also• Local adaptation based on

edge density• Global adaptation based on

number of candidate points

contrast calculation

locally adaptive contrast thresholding

candidate points

regions of interest (ROIs)

threshold adjustment

Too many or too few points? y

n

Post processing (ROIs)• Discard edges of clouds• Significantly reduces the

number of candidate points• Resulting few targets can be

tracked

n

cutting the perimeter of each object

histogram calculation

accept candidate point

Variance high?

reject candidate point

y

Post processing (ROIs)• Discard edges of clouds• Significantly reduces the

number of candidate points• Resulting few targets can be

tracked

n

cutting the perimeter of each object

histogram calculation

accept candidate point

Variance high?

reject candidate point

y

Post processing (ROIs)• Discard edges of clouds• Significantly reduces the

number of candidate points• Resulting few targets can be

tracked

n

cutting the perimeter of each object

histogram calculation

accept candidate point

Variance high?

reject candidate point

y

Post processing (ROIs)• Discard edges of clouds• Significantly reduces the

number of candidate points• Resulting few targets can be

tracked

n

cutting the perimeter of each object

histogram calculation

accept candidate point

Variance high?

reject candidate point

y

Example 1: Ground camera in hand

Pre- and post processing

Red points: all candidate objectsGreen point: allowed by post processing

Pre- and post processing + tracking

Red points: all candidate objectsGreen points: allowed by post processingBlue points: tracked objects

Example : Airborne camera

Navigation aid• UAV was equipped with INS and cameras

• Attitude was calculated from the INS data (blue)

• Attitude was calculated from 5 point (red) and 8 point (magenta) algorithms

• Visual-navigation data fusion development is going on

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Conclusion• Vision system is under development• Currently

• We are replacing the FPGA board• Finalizing the implementation of the image processing subsystem• Fusion of visual and navigation algorithms• Replacing the wVGA cameras with 1.2 megapixel ones

• Expecting the system completion by the end of the year

Thank you for your attention!

• The ONR Grant (Number: N62909-10-1-7081) is greatly acknowledged.

• The support of the grants TÁMOP-4.2.1.B-11/2/KMR-2011-0002 and TÁMOP-4.2.2/B-10/1-2010-0014 is gratefully acknowledged.

• The financial support provided jointly by the Hungarian State, the European Union and the European Social Fund through the grant TÁMOP 4.2.4.A/1-11-1-2012-0001 is gratefully acknowledged.

FPGA, many-core processing and UAV group

Ákos Zarándy Péter Szolgay

András Radványi

Zoltán Nagy

András Kiss

Pencz Borbála

Németh Máté