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1Scientific SystemsScientific Systems
Scientific Systems Company, Inc
Presentation at Meeting No. 96
Aerospace Control and Guidance Systems Committee
Harbour Town Resorts
Hilton Head, S. Carolina
19-21 October, 2005
By
Raman K. Mehra
Phone: 781-933-5355
Email: [email protected]
Scientific SystemsScientific Systems
VISTA: Visual Threat Awareness for Unmanned Air Vehicles
3Scientific SystemsScientific Systems
VISTA Overview
Problem: Situational awareness is required for “nap of the earth” UAV autonomy
Goal: Research, design and flight test a system for real time, visual collision obstacle detection
Proposed solution: Real time stereo + Perceptual organization + Image segmentation + Region tracking = VISTA (Visual Threat Awareness) system
4Scientific SystemsScientific Systems
Challenges of UAV visual obstacle detection
• 3D Reconstruction: Difficulty of computing 3D scene structure from two or more 2D images
• Performance tradeoffs
– Missed detections and false alarms vs. available computation
– Scene resolution vs. available computation
• Stereo vision: correspondence errors due to low contrast, occlusions, specular reflections, foreshortening, periodic features, threshold choice, minimum distance violation
– Traditional block matching stereo is too noisy for practical use in general!
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VISTA Achievements
• First application of 640x480@23Hz stereo in UAV flight
• Real time (3-5Hz) perception algorithm accelerated by Sarnoff Corp’s Acadia I vision processor for improved performance over traditional stereo
• Proof of concept visual obstacle detection and avoidance in two flight experiments on Georgia Tech’s GT-Max helicopter against rural obstacles
Obstacle Map: Green = Nearest Collision Obstacle, Yellow = Obstacle Disparity Map: Light = Close, Dark = Far, White = Undefined
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VISTA Architecture
• Stereo: Large baseline (50cm) binocular stereo for range measurement
• Foveation: Compression appropriate for collision detection
• Segmentation: Measurement fusion, Grouping for obstacle hypotheses
• Detection: False alarm rejection, Obstacle tracking
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Segmentation examples
Synthetic Sphere Synthetic Sign
Building Hallway
Large Tree Small Tree
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Flight experiment 1.1.10: Detection video
Obstacle Map: Green = Nearest Collision Obstacle, Yellow = Tracked obstacles
Disparity Map: Light = Close, Dark = Far, White = Undefined
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Flight experiment 2: Detection and avoidance
Flight 2.1.6 Flight 2.2.5
Flight Obstacle Time SpeedCollision
Volume
Keepout
Radius
Replanning
Radius
Detection Error @ Obstacle Distance
2.1.6 “Sign” 11:52 am 10 ft/s 60m/5m 40 ft 65 ft 7ft @ 65ft
2.2.5 “Sign” 3:23 pm 10 ft/s 60m/5m 40 ft 82 ft 15ft @ 82ft
Avoidance Triggered
Avoidance Triggered
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Flight experiment 2.1.6: Ground video
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Flight experiment 2.1.6: Detection video
Obstacle Map: Green = Nearest Collision Obstacle, Yellow = Tracked obstacles
Disparity Map: Light = Close, Dark = Far, White = Undefined
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VISTA Summary
Major Accomplishments:
• First application of 640x480@23Hz stereo hardware in UAV flight
• Real time (3-5Hz) algorithm that improves collision obstacle detection performance over traditional stereo only
• Proof of concept in two flight experiments against real obstacles.
We have demonstrated proof of concept for a real time visual collision detection system in a rural environment.