Mobile Vision for Autonomous…

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Presentation transcript:

Mobile Vision for Autonomous… Navigation and Reconnaissance Jay Silver Kevin Wortman Advised by Bill Ross Group 99

Vision-aided Navigation for Off-road Robotics ACC Testbed development and DARPA FCS-PerceptOR program Reconnaissance: Remote vision & monitoring, interactive map/video, and 3D modeling Navigation: Collision avoidance and improved position estimation Reconnaissance and navigation on limited payload platforms (UGV, UAV, etc.) Autonomous systems to support Future Combat Systems (DARPA TTO, PerceptOR) Vision systems for time-to-goal/bandwidth-constrained collision avoidance in unstructured outdoor environments (example: lightly wooded scenarios) All-terrain robotic vehicle (Ruggedized, 2 meter/sec) Digital fisheye video (180 fisheye,12bit, 60fps, Megapixel) 6-DOF IMU (134 Hz, 3 gyros + 3 accelerometers) Wireless ethernet (802.11 standard) Onboard compute (2 Pentium IIIs) Offboard processing (Dual Pentium IV) Robotic Testbed (ACC - 9332) Group 99 1 1

Autonomous Navigation Group 99 1 1

Navigation Resolution = f ( polar radius ) Many resolutions to create new perspectives Path finding with probabilistic obstacle models Group 99

Navigation Resolution = f ( polar radius ) Many resolutions to create new perspectives Path finding with probabilistic obstacle models Group 99

Space Variant Resolutions I(x,y) I(r,q) Distorted depth/range perception r q } Standard Fisheye Res = constant q r Accurate depth/range perception Space Variant Res = f ( r ) Group 99

Space Variant Resolutions I(x,y) I(r,q) Distorted depth/range perception r q } Standard Fisheye Res = constant q r Accurate depth/range perception Space Variant Res = f ( r ) Group 99

Space Variant Resolutions Group 99

Space Variant Resolutions Resolution function is the integral of the angular shift w.r.t. eccentricity Space Variant Resolution = Approximate with sum and find a least squares best fit polynomial Group 99

Space Variant Resolutions Equal shifts along the radius of expansion for objects with equal range. Boundary tracking is simplified. Bird’s Eye View of Angular Shift Red = High Angular Shift Blue = Low Angular Shift Space-variant resolution function Inverse Resolution - degrees/pixel Sensor limit Eccentricity - degrees Group 99

Space Variant Resolutions Fixed resolution Space-variant resolution Group 99

Navigation Resolution = f ( polar radius ) Many resolutions to create new perspectives Path finding with probabilistic obstacle models Group 99

Navigation Resolution = f ( polar radius ) Many resolutions to create new perspectives Path finding with probabilistic obstacle models Group 99

Multiple Resolutions Red = high res. Green = med res. (1/2) Blue = low res. (1/4) Red = high res. Green = med res. (1/2) Blue = low res. (1/4) Group 99

Navigation Resolution = f ( polar radius ) Many resolutions to create new perspectives Path finding with probabilistic obstacle models Group 99

Navigation Resolution = f ( polar radius ) Many resolutions to create new perspectives Path finding with probabilistic obstacle models Group 99

Path Finding with Probabilistic Obstacle Models Danger Avoidance heavily rewarded Minimum time to goal heavily rewarded Group 99

Ideal Path Finding with Probabilistic Obstacle Models Group 99

Reconnaissance Navigation Resolution = f ( polar radius ) Many resolutions to create new perspectives Path finding with probabilistic obstacle models Access this presentation tomorrow at: www.jaysilver.net/presentations Next: Reconnaissance Group 99