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    TARDEC Presentation 2 TARDEC Presentation 2 Presentation Transcript

    • Technology OverviewChris SlaughterPresidentProf. Sriram VishwanathChief Scientist
    • Our Team 2 Prof. Sriram Vishwanath • 9 years, Prof., UT Austin • Information Theory, Entrepreneurship • 2 Robotics Labs: MA coordination and 3D Perception Chris Slaughter • PhD. Candidate, Electrical Engineering • 1.5 years consulting T.O. of Austin Startup • Research Lead, UT Perception Laboratory Ongoing Collaborations/Partnerships • Lockheed Martin • HKS • NVIDIA • UC Berkeley
    • Our Team 3• Computer Vision: Multi-view geometry and stereo; tracking; 3d reconstruction• High Performance Computing: General purpose graphics programming (GPGPU), parallel/distributed computing, heterogeneous computation• Statistics and Learning: Large scale clustering problems, compressive motion analysis, graphical inference• Embedded Systems: Board design, multi-processor interaction, interfaces, power/weight/form factor
    • Mission Statement 4 To Teach Unmanned Vehicles to See as Humans Do Applications: • Absolute localization in GPS denied scenarios • Visual tracking odometry • Landmark detection and landmark-based navigation • Terrain mapping and change detection for IED disposal • Immersive visualization for situational awareness • Disaster response
    • System Architecture 4 Server Nodes Producer Nodes Consumer Nodes
    • System Architecture 5 Server Nodes Producer Nodes Consumer Nodes
    • Producer Nodes
    • Producer Nodes 7 Computing Element: Embedded GPGPU processor ARM multi-core Heterogeneous architecture Optoelectronic Device: Active Stereo (IR) Passive Stereo Laser / TOF
    • Producer Nodes 8 Key Features: • Visual Odometry ( < 3 mm ) • Mapping • Landmark Extraction • High Data Rates ( 9.2M pts / sec ) • Volumetric / Manifold Reconstruction • Source Compression for Uplink Compatibility: • Low power ( < 4.5 W ) • Low weight ( cell phone – battery ) • Low cost ( COTS sensors )
    • Producer Nodes 9 Signal Processing • Fundamental task in vision-based algorithms • Most algorithms too slow even for desktops • Bilateral filters • Pyramid computation • Depth-RGB conversion • SIFT descriptors • Object recognition
    • Image Filtering
    • Pyramid Computation
    • Frame Matching
    • Producer Nodes 1 3 Visual Odometry • RGB-based • 30 FPS • Accuracy in mm • Compressive sensing solution • Range-based • 9.2M pts / sec • 90T comps / sec • Towards drift free?
    • Vision-Only Tracking
    • RANSAC (1983)
    • RANSAC (1983) SOLO (2010)
    • Producer Nodes 1 7 Can a mobile device map 3D environments to produce dense 3D data points rather than sparse landmarks? Dense Reconstruction • Landmark-based mapping (1960’s – present) – SOLVED • DARPA grand challenge • Efficient global alignment (1999 – 2005) – SOLVED • Multiple view geometry + dense reconstruction (1980’s – present) • Live dense reconstruction (2006 – present) • KinectFusion • Range-based SLAM • Dense Tracking and Mapping
    • Volumetric Reconstruction
    • Point Cloud Reconstruction
    • Patchwork Reconstruction
    • Depth Data
    • Producer Nodes 2 2 Patchwork Reconstructions • More memory efficient than volumes • Faster integration and tracking • Efficient caching interplay • Global refinement • Runs on embedded device
    • Server Nodes
    • Server Nodes 2 4 Global Refinement • Maps must be globally consistent • Dense reconstruction doesn’t allow for this refinement • Patchwork generalizes to this functionality • Inherently multi-core problem • ARM architecture for GraDeS
    • No RefinementHOGMAN (2007)
    • Full Visualization 2 6 Custom Visualization • Key task: visualization • Interactivity (XBOX controller) • Full support for 3d / 2d streams • Event processing • Interoperability with GPGPU (CUDA) • Compression for consumer nodes • Fully scalable Rendering Pipeline • Custom rendering pipeline for Patchwork • Volumetric and Point Cloud • Raycasting with lighting sources
    • Consumer Nodes
    • Consumer Nodes 2 8 Our technology can produce maps at high speeds and unprecedented fidelities But.. What to do with this content? Visual Localization Situational Awareness• Localization a major problem in GPS- • Visualize mapping assets in real time denied scenarios from cell phones • “Urban canyons” • Coordinate with server and receive • Indoor environments compressed video stream • MAV / UGV coordination • Back-end models dynamics of• Existing solutions based mostly on adversaries state estimation • Extensible visualizer: new• Possible to query large maps for tags, models, data sources location?
    • Visual Absolute Localization
    • Feasible Path Infeasible Path
    • Positional Decoding
    • Conclusion 3 2 Current trends in computer vision and robotics: • High performance computing • Live dense reconstruction • Range-based tracking and mapping Our architecture: • Producer nodes: • COTS sensors • Commodity computational unit • Dense tracking and mapping • Server nodes • Combine producer data into large maps • Serve consumer nodes • Consumer nodes • Visual absolute localization and remote visualization
    • Thanks!