uav visual saa on many core processorsusers.itk.ppke.hu/~zseta/pa/uav_1sl.pdf · 2014-12-01 ·...
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Pázmány Péter Catholic University, Faculty of Information Technology and Bionics
UAV Visual SAA on Many CoreProcessors
Tamás Zsedrovits
[email protected]/~zseta
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Outline
• Goals
• Sense and avoid problem
• System requirements
• Closed loop visual SAA system
• Vision system architecture
• Algorithms
• Many core processor array implementation
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Goals
• Unmanned Aerial Vehicle (UAV) technology is maturing– UAVs are ready for autonomous missions technically
• Surveillance, firefighting, agriculture, etc.
– Autonomous missions are not authorized
• Missing redundancy
• Missing collision avoidance device
– Automatic collision avoidance device is needed even for remotely piloted UAVs
• Remote aircraft detector sensor development is introduced in this presentation
„When Will We Have Unmanned Commercial Airliners?”
IEEE Spectrum MagazineDecember 2011
Sense and avoid –key issue
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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
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Requirements
• Detect a 10m aircraft from 2km
• 0.1 degree/pixel resolution
• min. 220x60⁰ view angle
• Flyable size/weight/power figures (@25Hz, <1W, <500g)
• On-board data storage
intruder
collision
volume separation
minima
collision
avoidance
threshold
traffic avoidance
threshold
our
track
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Closed loop SAA system
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HIL simulator
Estimation
Estimation
algorithms
(FPGA)
Image
Rendering
and
Processing
FlightGear
(PC)
Image
processing
(PC)
y,z,a (USB)[X,V,b]+P
(USB)
True and Estimated
Trajectories,
Estimation error
Aircraft
cockpit A/C
Control
Ctrl. Defl.
(Serial link)
Flight control
(MPC 555)
Aircraft
Model (PC-
Simulink)
Ctrl. Defl
(PWM)
[X,V,b]+P
(Ethernet)
True+Est.
A/C info packet
(Ethernet)
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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
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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
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Sensor and computational system
• 5 pieces of wVGAmicro 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
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Mechanics
• Stable camera holder
– Alignment
– Avoids cross vibration of the cameras
– 100g
– Aluminum alloy
– Electronics in the middle
– Covered with aluminum plates
11
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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
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Vision system mounted to the airplane
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View angle
Panoramic view (stitched images)
Perspective view
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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
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First picture?
Adaptive threshold ROI
Yes
ROI calculation and cut
Adaptive
threshold darker
OR
Recall
Output data
CutNo
Input images
Invert
Adaptive threshold
brighter
Closing
Detection against the blue sky
Y=1020 Z=255 Wingspan=42
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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
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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
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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
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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
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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
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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
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Tracking on image plane
• Candidate objects:
– Separate objects
– Track objects
• Filtering according to:
– Velocity, acceleration
• InstantVisionTM 4.1 Multitarget Tracking Library (Eutecus Inc.)
Distance calculation
Measurements
Gating
State Estimation
Data Association
Target positions
and attributes
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Example 1: Ground camera in hand
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Pre- and post processing
Red points: all candidate objectsGreen point: allowed by post processing
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Pre- and post processing + tracking
Red points: all candidate objectsGreen points: allowed by post processingBlue points: tracked objects
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Example : Airborne camera
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Virtual Cellular Machines
• Many core processors
• Virtual memory concept
• Hide the hardware details
– Build a more general atchitecture (FPGA, GPU…)
– Make easier for the programmer
– The hardware layer can be changed
Algorithm VCMManyCore
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SAA algorithm VCM synthesisBGR
Algorithmic description Virtual machine Physical machine
2048x1024@25FPS
Noise reduction
Adaptive thresholds
Spatial-logic combination
ROI selection
Cloud analysis Areal feature
point analysis
Tracking
Streaming (pipe-line)
architecture
RISC
Coarse grain
architecture
Streaming (pipe-line)
architecture
2048x1024 cellular
processor array at
3700 Array_Op/sec,
150MPixel/sec
RISC
64x64 cellular
processor array at
65000 Array_Op/sec
RISC
• Topographic, vector and scalaroperations:
– @25 fps ~3700 pixel level operations
– Transformation to streaming processors
– ASIC or FPGA implementation
– Cellular processor array – small loadlarge area
• Scalar dominant operations
– ROI selection, tracking
– Implementation on RISC cores
• Topographic operations with smallframe size
– Only 64x64 pixels/window
– @25 fps ~65000 operations
– Coarse grain implementation (Xenon)
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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
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Image processing system
Memory
controllerDRAM
Microblaze
processor
Image capture
Full frame
preprocessing
Gray scale
processor
Binary
processor
• Input:
– DVI/HDMI
• 1920x1080@50Hz
– Native camera interface
• Three image processing accelerators
• Parallel operation
• Optimized for the image processing algorithm
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Full frame image preprocessor
DVI Interface
Adaptive
threshold
Centroid
computation
Input Video
1920x1080@50Hz
FIFO
Microblaze FSL
R
FIFOG
FIFOB
FIFOBW
CE
CE
Memory
Interface
Memory
clock domainDVI clock
domain
• Adaptive threshold
– 3x3, 5x5, 7x7 neighborhood
– Programmable threshold level
• Centroid computation
– 32x32 centroid
– Final 128x128 centroid computed by Microblaze
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Gray scale processor
Mem_0
128x128
8bit
Mem_1
128x128
8bit
Mem_N
128x128
8bit
PE_0 PE_1 PE_M
Input_1 Buffer Input_2 Buffer
Port A Port A Port A
Port B Port B Port B
A DI DO A DI DO A DI DO
I1_sel I2_sel
O_sel
Microblaze
clock
domain
Image proc.
clock
domain
• On-chip memories– 128x128 pixel images– 8 RAMB18– Separate port for fast
Microblaze access
• Separate clock domain• Specialized Processing
Elements (PE)• Supported operations:
– Edge Average, Diffusion, Threshold, Pixel wise arithmetic operations
– Global white, black, change
• Expected performance:– 150MHz, 27.3us/op,
1460op/frame
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Binary processor
Mem_0
128x128
1bit
Mem_1
128x128
1bit
Mem_N
128x128
1bit
Input_1 Buffer Input_2 Buffer
Port A Port A Port A
Port B Port B Port B
A DI DO A DI DO A DI DO
I1_sel I2_sel
Microblaze
clock
domain
Image proc.
clock
domain
128bit data
path
PE_0 PE_1 PE_M
O_sel
128x1 processor array
• On-chip memories– 128x128 pixel images– 1 RAMB18– Separate port for fast
Microblaze access
• Separate clock domain• One line of Specialized
Processing Elements (PE)• Supported operations:
– Erosion, Dilation, Recall, Pixel wise logic operations
– Global white, black, change
• Expected performance:– 150MHz, 0.85us/op,
46,800op/frame
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Vector processing unit for Kalman filter
64x64bit
Vect_0
*
+
D
A DI
DO
ReadAddrWriteAddr
64x64bit
Vect_1
A DI
DO
64x64bit
Vect_n
A DI
DO
Scratchpad
memory
DI DO
A DI DO
Microblaze clock
domain
• Tracking and 3D estimation• On-chip memories
– Scratchpad memory– Separate port for fast
Microblaze access – 64bit wide 64 element vector
registers
• Configurable vector length• Separate clock domain• 64bit double precision
floating point units• Supported operations:
– Independent multiplication and addition
– Multiply and add (MADD)
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References
• T. Zsedrovits, A. Zarandy, B. Vanek, T. Peni, J. Bokor, and T. Roska, “Collision avoidance for UAV using visual detection,” in Proc. of 2011 IEEE Int. Sym. of Circuits and Systems (ISCAS), 2011, pp. 2173–2176.
• T. Zsedrovits, A. Zarandy, B. Vanek, T. Peni, J. Bokor, and T. Roska, “Visual Detection and Implementation Aspects of a UAV See and Avoid System,” in Proc. of 2011 20th European Conference on Circuit Theory and Design (ECCTD), 2011, pp. 472–475.
• Z. Nagy, A. Kiss, A. Zarandy, T. Zsedrovits, B. Vanek, T. Peni, J. Bokor, and T. Roska, “Volume and power optimized high-performance system for UAV collision avoidance,” in Proc. of the 2012 IEEE Int. Symp. on Circuits and Systems, 2012, pp. 189–192.
• A. Zarandy, T. Zsedrovits, Z. Nagy, A. Kiss, P. Szolgay, and T. Roska, “Cellular processor array based UAV safety system,” in Proc. of 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications, 2012, pp. 1–2.
• A. Zarandy, Z. Nagy, B. Vanek, and T. Zsedrovits, “A five-camera vision system for UAV visual attitude calculation and collision warning,” Comput. Vis. Syst. Lect. Notes Comput. Sci., vol. 7963, pp. 11–20, 2013.
• A. Zarandy, M. Nemeth, Z. Nagy, A. Kiss, L. Santha, and T. Zsedrovits, “A real-time multi-camera vision system for UAV collision warning and navigation,” J. Real-Time Image Process., Sep. 2014.