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Overview of Autonomous Vehicle Research Activities at Adelaide University

The Sensor Signal Processing Group at Adelaide University, led by Prof. D.A. Gray, focuses on advanced signal and information processing for autonomous vehicles. The team, comprising 4-5 researchers and PhD students, specializes in various sensors including radar, electro-optical, LIDAR, and GPS/INS integration. Research activities include target classification, scene analysis, jam-resistant GPS techniques, and sensor fusion for path planning and collision avoidance. With ongoing projects in UAV interference mitigation and terrain analysis, the group is at the forefront of technological advancements in autonomous vehicle applications.

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Overview of Autonomous Vehicle Research Activities at Adelaide University

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  1. Sensor Signal Processing Group (EEE, Adelaide Uni) Overview of autonomous vehicle related activities D.Gibbins, October 2010

  2. SSP Group Overview • Team of 4-5 researchers plus Phd Students (Research Leader: Prof. D.A.Gray) • Specialising in Signal (& Information) Processing • Radar (L-band, SAR, ISAR , phased-array, MIMO) • Electro-optical, LIDAR/LADAR, Sonar sensors etc.. • GPS/INS • Target classification, recognition, 2D image and 3D scene analysis, route planning etc • Focus on applications related to Autonomous vehicles • GPS Anti-jam, jammer localisation (single/multiple UAV’s) • Sensor fusion, path planning using PMHT, SLAM etc... • Terrain & scene analysis • Target recognition (2D & 3D) – apps in aerial surveillance • Radar sensors for autonomous vehicles (research interest) • Detection/mapping/collision avoidance?

  3. Unprotected Receiver Measurements Protected Receiver Measurements East Measured Van Location South GPS Principle Researcher: Matthew Trinkle Conventional and improved interference localisation • Interference Mitigation & Localisation for UAV applications • Temporal, spatial and STAP processing • Adaptive beam-forming • Null steering • DOA estimation • Successful anti-jam trials held in Woomera in presence of multiple interference sources • Ongoing development of compact anti-jam hardware for aerial platforms

  4. UAV surveillance & targeting Principle Researcher: Danny Gibbins • Electro-optical Seeker Target Recognition (DSTO sponsored) • Static land based & littoral moving targets etc • LADAR/LIDAR terrain reconstruction and classification (DSTO sponsored) • Stabilisation, reconstruction & scene analysis for apps such as route planning, situation awareness etc • LADAR/LIDAR 3D target recognition (DSTO & self funded R&D) • ICP registration, SIFT matching, correlation based etc (high res and more recently low-resolution data) • Video based stabilisation/super-resolution/geo-location (DSTO sponsored)

  5. EO Mid-course Navigation, LADAR Terrain Analysis & Classification 3D Terrain reconstruction from airborne LADAR & optical data Example of EO Model Recognition for navigation correction – Real Data “A Comparison of Terrain Classification using Local Feature measurements of 3-Dimensional Colour Point-cloud Data” D.Gibbins IVCNZ 2009.

  6. 3D LADAR/LIDAR Target Recognition (& registration) 3D Sift feature analysis 3D Sift feature matching “3D Target Recognition Using 3-Dimensional SIFT or Curvature Key-points and Local Spin Descriptors” D.Gibbins DASP 2009.

  7. PMHT Path Planning for UGV’s (Cheung,Davey,Gray) x01 x11 xT1 Platform States • Probabilistic multi-hypothesis tracking for UGV path planning • Treats locales of interest as measurements and UGV platforms as targets • Attempts to optimise search across multiple UGV’s x0m x1m xTm z1 z2 zn Waypoints  Example of path planning for 4 UGV’s based on random locations of interest Waypoint to platform assignments k1;πk k2;πk kn;πk τ1;πτ τ2;πτ τt;πτ Waypoint to time assignments

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