CNES robotics activities Perception and autonomous navigation for planetary

missions

Michel DELPECH (on behalf of CNES robotics team)

ASTRA 2015 – 11/05/2015 2

Contents

Introduction (context / definition) CNES activities (core expertise) Rover Perception / Localisation / Navigation Test & validation facilities

Application to others domains Support for lander mission (Rosetta / Philae) Navigation R&D on landing (planets and asteroids)

Conclusion

3

Context

■ Background : 20 years of development in autonomous navigation for planetary exploration rovers - Small team (10 people including students) mainly focused on R&D activities ■ Current work context: Collaboration with ESA for the ExoMars mission Prospective R&D for future missions

■ Objective = extend autonomous navigation capabilities for long range traverses Progress is required in 2 major areas: Functional Layer: Perception / Localization / Path Planning / Path execution Decision Layer: High level planning – Resource monitoring - Recovery if anomaly

CNES focus: extend the capabilities of the Functional layer to limit the assistance of the Decision Layer to “real” anomaly situations

ASTRA 2015 – 11/05/2015

Matching Image acquisition

3D modelling DEM Depth

image Stereo images

Map generation

Path planning

Perception scheduling

Path tracking

Locomotion

Localization

Target

Traject. Nav map

Cmd

Optimized algorithms: ready to fly” at functional level and evaluated on LEON 2 CPU

Dynamics

Robot State

Loc. sensor acquisition

Autonomous Navigation loop

ASTRA 2015 – 11/05/2015

5

Perception (1/3)

■ Stereovision systems Mechanical design expertise Wider FOV cameras for future missions

■ Tools for tests & validation MGSE = precise 3D measurement

(high resolution laser scanner + collimator) Optimized characterisation process Stereo base measurement methods

■ Algorithms suite Fast stereovision (multi-resolution) Optimized algorithms

ASTRA 2015 – 11/05/2015

6

Perception (2/3)

■ Adaptations for configurations with more available memory (ExoMars baseline) Hi-dynamics cameras (>8bits)

• Robustness • Radiometric correction

Algorithms improvement • Spare computation time (ex : images rectification) • 3D reconstruction with higher accuracy

(full resolution processing)

Illustration

ASTRA 2015 – 11/05/2015

ASTRA 2015 – 11/05/2015 7

■ R&D studies on large field-of-view stereovision Evaluation and calibration of different imaging devices

• Hyperboloidal mirrors • Fisheye cameras

Studies on image resolution, sampling and depth-of-field Comparison of different image rectification techniques 3D reconstruction from image pairs

Perception (3/3)

8

Localisation activities

■ Visual Motion Estimation (VME) Algorithms adaptation for increased OB

memory • Higher accuracy & lower CPU load

Test & validation with images taken on-board a moving rover Characteristics

• Data fusion dead-reckoning/VME/sun sensor

• < 1% localization over 100 m • 1.5 deg azimuth (update with Sun

sensor twice a day)

Current studies on SLAM hybridization

Odometry filter (EKF)

Locomotion sensors

VME

Inertial sensors (IMU) + images

Sun / stellar sensors

6D position

T=160 ms

T=10 s

T=160 ms

T=? s

ASTRA 2015 – 11/05/2015

9

Current Navigation activities (1/4)

■Algorithms adaptation for increased OB memory Increase of the NavMap size :

• local knowledge (~14x14m) • → up to 1 sol traverse (~100x100m)

NavMaps = bitmaps directly issued from DTM

■Short term Path Planning (evolutions) PP under kinematic constraints

• Traj = curve instead of straight lines FRA*, D* algorithms available Map building: rover cross capabilities better taken into account Kinematics + clearance

Forbidden

Forbidden for planning Navigable

ASTRA 2015 – 11/05/2015

Path planning

10

■ Long Range Navigation (efficient map building and path planning) Obstacles Map : Vectorial representation of obstacles from NavMaps that are updated at each Path & Perception Planning step (~4m) Obstacles Map store all obstacles of mission Obstacles map can be pre-initialized with orbiter data and avoidance areas Algorithms compatible with currently available memory & CPU Major pros :

• Low memory usage • All previous knowledge available at each PP step

– avoid dead-end loops, – enable (automated) secured return to landing site

Work consolidated through A. Ruzu thesis

Current Navigation activities (2/4)

ASTRA 2015 – 11/05/2015

11

Current Navigation activities (3/4)

Long Range Navigation

Bitmap to

Obstacles Map (vectorial

representation)

ASTRA 2015 – 11/05/2015

12

Current Navigation activities (4/4)

Size compression > 100 x

Long Range Navigation 100x100m map 4 cm resolution

Rover position

End goal

SEROM obstacle map from previous exploration or acquired by Orbiter

Two steps at each iteration: • Long range planning to

update the path and select a sub-goal

• Short range planning

ASTRA 2015 – 11/05/2015

SEROM testing field

13

Mapping & localization

■2 types of perception sensors : 3 stereovision systems (Navcams, LocCams,

ZoomCams) 1 LIDAR

■Goals : Evaluation of 3D reconstruction algorithms

with complementary localization sources Data fusion design and optimization

Cameras Lidar

Resolution 1280 x 1024 x 2 32 x 2250 x 1

Acquisition 5 MB 0.2 MB

Spatial cov. 4 x 4 m 40 x 40 m

Nb of pts 26 kpts 36 kpts

CPU +++ --

R&D activity with ONERA (French Aerospace Lab)

Example of LIDAR acquisition around the rover (3x3m) ASTRA 2015 – 11/05/2015

14

Means for Navigation activities (1/5)

■ SEROM 80 x 50 m outdoor test field Volcanic soil with various

obstacles sizes Also 20 x 20 m hangar for

indoor tests Already used for ExoMars

remote experiments with ESA and partners

ASTRA 2015 – 11/05/2015

15

■ IARES Rover delivered in 1997 (VniiTransmash) 17 + 2 d.o.f Still used for tests

Means for Navigation activities (2/5)

ASTRA 2015 – 11/05/2015

16

■ ARTEMIS New rover demonstrator with Exomars-like

kinematics (6 wheels on 3 rocker bogies) New locomotion control system based on

off-the-shelf components Hardware integration is finished and

locomotion has been validated on SEROM test site Current integration into the CNES

Autonomous Navigation SW test framework

Means for Navigation activities (3/5)

ASTRA 2015 – 11/05/2015

17

■ Aerial photography & DEM reconstruction Multi-rotor helicopter with stabilized pod for

aerial imaging on test site Software able to reconstruct Digital Elevation

Model from aerial, sequential images Used to test vision-controlled landing techniques

and to provide site maps to rover navigation software

Reconstructed DEM Fly path

Means for Navigation activities (4/5)

ASTRA 2015 – 11/05/2015

18

■ Rover simulator with advanced features Fog, Stones, Real time shadowing, MNT refreshment,

Means for Navigation activities (5/5)

■ Navigation SW test framework analysis tools for easy SW validation operator remote control station

ASTRA 2015 – 11/05/2015

19

Remote testing capabilities

The adaptation of the C&C architecture to allow rover operation from ESTEC by scientists (Exomars context) is under analysis Further experiments can be envisioned ….

The testing facilities can host experiments to be performed from remote locations Experiments have already been achieved in tele-operation from ESTEC ■ Autonomous navigation campaign (2010) ■ Remote operations with scientific instruments

(2011)

ASTRA 2015 – 11/05/2015

20

■Lander first touchdown localization

operations by ROLIS/OSIRIS image & DTM registration (within one hour after touchdown)

■ Lander first rebound trajectory waypoints extraction from Philae's shadow observed on orbiter images taken during rebounds

■ Local DTM reconstructions from ROLIS CIVA-P and ROLIS-CUC images (by stereo-photogrammetry) - Lander movements and drilling trajectory reconstruction from ROLIS-CUC images on final landing site

Rosetta/Philae activities

Support during Philae operations

ASTRA 2015 – 11/05/2015

21

■ Navigation during landing phase on planets or asteroids Adaptation of in-house algorithms (ex: VME) Evaluation of new feature detectors / filtering

techniques Performance assessment on outdoor images with

UAV

■ Navigation for proximity operations around asteroids Feasibility analysis of close range orbiting (gravity

field characterization of Apophis asteroid) Evaluation of navigation techniques in presence of

high gravity uncertainty hybridization of EKF-SLAM / bundle adjustment

Navigation during EDL

R&D activities

ASTRA 2015 – 11/05/2015

22

Conclusion

■ Continuous improvement made on the CNES core field of expertise: Perception / Localization / Map building / Path planning

■ CNES remains open to collaborative work with international partners expertise in perception / autonomous navigation software framework testing facilities (incl. rovers and optical characterization tools)

■ Adaptation of these technologies/subsystems to other/various robotics applications/missions : Ground modeling (landing, probe approaching phase, ...), In orbit modeling (non cooperative objects, ...), Short & long distance localisation (landing, robotics in orbit, ...),

ASTRA 2015 – 11/05/2015

23

Thank you for your attention

ASTRA 2015 – 11/05/2015