Overview of autonomous vehicle related activities

D.Gibbins, October 2010

Sensor Signal Processing Group, EEE dept, Adelaide Uni, October 2010

SSP Group OverviewTeam 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 etcFocus 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?

Sensor Signal Processing Group, EEE dept, Adelaide Uni, October 2010

Angular Separation of Sources (degrees)

Inte

rfer

ence

DO

A (

degr

ees)

Beampattern as Mobile Interference Approaches North Camp

10203040506070

0

20

40

60

80

100

120

140

160

180

Angular Separation of Sources (degrees)

Inte

rfer

ence

DO

A (

degr

ees)

Beampattern as Mobile Interference Approaches North Camp

10203040506070

0

50

100

150

Conventional and improved interference localisation

GPS 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

Principle Researcher: Matthew Trinkle

-8000 -6000 -4000 -2000 0 2000 4000 6000

-2000

0

2000

4000

6000

8000

10000

12000

SouthE

ast

Measured Van Location

Unprotected Receiver Measurements

Protected Receiver

Measurements

Sensor Signal Processing Group, EEE dept, Adelaide Uni, October 2010

UAV surveillance & targetingElectro-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)

Principle Researcher: Danny Gibbins

Sensor Signal Processing Group, EEE dept, Adelaide Uni, October 2010

“A Comparison of Terrain Classification using Local Feature measurements of 3-Dimensional

Colour Point-cloud Data” D.Gibbins IVCNZ 2009.

EO Mid-course Navigation, LADAR EO Mid-course Navigation, LADAR Terrain Analysis & ClassificationTerrain Analysis & Classification

Example of EO Model Recognition for navigation correction – Real Data

3D Terrain reconstruction from airborne LADAR & optical data

Sensor Signal Processing Group, EEE dept, Adelaide Uni, October 2010

3D Sift feature analysis

3D Sift feature matching

3D LADAR/LIDAR Target Recognition (& 3D LADAR/LIDAR Target Recognition (& registration)registration)

“3D Target Recognition Using 3-Dimensional SIFT or Curvature Key-points and Local Spin

Descriptors” D.Gibbins DASP 2009.

Sensor Signal Processing Group, EEE dept, Adelaide Uni, October 2010

PMHT Path Planning for UGV’s (Cheung,Davey,Gray)

Probabilistic multi-hypothesis tracking for UGV path planningTreats locales of interest

as measurements and UGV platforms as targets

Attempts to optimise search across multiple UGV’s

z1 zn

k1;πk kn;πk

Waypoint to platform

assignments

Waypoints

Platform

States

x01 x1

1

τ1;πτ τt;πτWaypoint to

time assignments

x0m

xT1

xTm

z2

k2;πk

τ2;πτ

x1m

Example of path planning for 4 UGV’s based on random locations of interest