From Cars to Mars – applying Autonomous

Vehicle Navigation Methods to a Space Rover

Mission

Dr Brian Yeomans

Oxford Robotics Institute

http://ori.ox.ac.uk/

Presentation

Outline

● About ORI

● The MURFI 2016 mission

● Dub4 and Experienced Based Navigation

● Monocular navigation and localisation

● Dense Reconstructions

● Conclusions and Next Steps

ORI’s mission Develop technology that allows machines to ask and answer:

“Where am I?” “What surrounds me?” “What should I do next?”

ORI Core Tech

Key elements of our autonomy system: • Divides into several different topics • All platforms share a common

codebase • Targets for integrated system:

• Infrastructure-Free • Operate over Large Scales • Low Cost

Unmanned Platforms

Manned Platforms

Sensors ● Cameras (Stereo and

mono)

● Lidar (3D and 2D)

● Radar

● Ultrasonics

● INS

What do we do with our data?

● Processing... ○ Compression, Indexing,

Visual Odometry

○ Scene Priors, FABMAP

Loop Closures, Global

Optimisation, Map Building

○ BORG, YOLO, Global Map

Database, ...

● Sharing... ○ http://www.ori.ox.ac.uk/datasets/

○ RobotCar Dataset

○ FABMAP Eyensham 70k Dataset

○ New College Vision and Laser

Dataset, ...

● Experiments... ○ Testing for navigation,

planning, ...

○ Training for perception &

planning

○ Simulation for system

design and testing

○ ...

The MURFI 2016 Mission

● Run by UKSA in collaboration with CSA in

two locations, Utah, USA and Harwell, UK

● ExoMars like analogue mission designed

to demonstrate mission capability

MURFI Rover Hardware and

Sensors

4 wheel steering, passive

dynamic suspension

Bumblebee XB3 stereo

camera Long range Wifi (1km +)

Mast mounted PanCAM

(Univ of Aberystwyth)

3D LIDAR Monocular camera

Other instruments as required

MURFI Sol drives

● Planned tracks simple - linear drives linked by

point turns

● Egomotion of Rover generated using Oxford VO

● Differential controller keeps Rover on track Image credit MURFI team

Oxford Visual Odometry

● Flying on

ExoMars

Dub4 / EBN

● “Wherever, Whenever, Whatever the Weather” (dub4)

● Teach and Repeat

● Experience stores visual snapshots plus relative coordinates between

snapshots

● More drives -> more experiences. Improve localisation irrespective of

viewpoint, illumination, weather

● Mapping:

○ Uses Oxford VO to estimate trajectory

○ Each stereo image pair creates a bank of landmarks and the 6-

DoF transform from the previous image pair

○ Stored in graph where nodes are the landmarks and the 6-DoF

transforms the edges.

Dub4 / EBN (cont)

● Localisation:

○ Two stage process: place recognition using FABMAP

○ Local-scale localisation generates 6-DoF transform with the

most inliers

● Path memory

○ Resource requirements reduced using a ranking policy based

on the distance between candidate experience nodes and the

current VO-based position estimate

● Illumination invariance

Dub4 Performance

Dub4 Performance

Dub4 in a Space context

● Performs well in the current configuration

● Optimise for planetary surface environments:

○ Improve illumination and viewpoint

invariance

○ Feature detectors optimised for low-featured

environments

Single Camera Navigation

● Wide Angle monocular camera pointing down

● Track ground texture in front of rover

● Nodes store features - edges represent transforms

between nodes (like Dub4)

● Approximately planar assumption - derive planar

induced homography

● Outlier rejection with RANSAC

Single Camera Navigation (cont)

● Used SURF here but others are possible, SIFT, BRIEF

etc

● Recover motion from homography using SVD - reject

unfeasible solutions

● Maintains regular localisation (no dropouts)

● Multiple experiences - drift between runs but can

localise and repeat the path

Mononav Performance

Dense Reconstructions

Dense Reconstructions

Dense Reconstructions

Dense Reconstructions

● Feature rich, lifelike 3-D

meshes

● Tool for mission scientists

● Machine Learning

● Advanced Autonomy

Conclusions and Next Steps

● Oxford VO performance

● Dub4 performance

○ potential to enhance with minor modifications

● Single camera localisation performance

○ minor modifications only

● Dense Reconstructions

○ Science visualisation tool

○ Enhanced AI

○ Robot geologist