VALIDATION OF 3D ENVIRONMENT PERCEPTION FOR LANDING ON SMALL BODIES USING UAV PLATFORMS

Transcription

1 ASTRA 2015 VALIDATION OF 3D ENVIRONMENT PERCEPTION FOR LANDING ON SMALL BODIES USING UAV PLATFORMS Property of GMV All rights reserved

2 PERIGEO PROJECT The work presented here is part of the PERIGEO project Co-funded under national spanish INNPRONTA program Objective of the project: Investigate use of UAV as validation platform for the development of space technologies GMV and CATEC (R&D center in Seville) worked together on Autonomous navigation techniques On-board autonomy Optic navigation PERCEPTION FOR NEO LANDING 12/05/2015 Page 2

3 OBJECTIVE OF THE ACTIVITY Within autonomous navigation, the objective has been to: Research algorithms for 3D imaging sensor relative and absolute navigation (to be used in planetary approach and landing scenario on a Near Earth Object) Motivation: current baseline is camera + radar altimeter as navigation sensors for approach and landing. New trend towards less power-hungry 3D imagers. Can they be used for navigation on deep space missions? Development of the selected algorithms in synthetic environment (software-based) Validation of the algorithms with a representative sensor onboard a UAV Are UAV a valid mean to raise TRL for GNC technologies? PERCEPTION FOR NEO LANDING 12/05/2015 Page 3

4 DEVELOPMENT & VALIDATION ENVIRONMENT /1 A data generator (that simulates the 3d imaging sensor) and the corresponding algorithms have been designed and tested Testing has been carried our first on simulation and then on UAV Data has been used from ITOKAWA asteroid PERCEPTION FOR NEO LANDING 12/05/2015 Page 4

5 DEVELOPMENT & VALIDATION ENVIRONMENT /2 ROS has been used as software development environment ROS-GAZEBO as visualization tool Algorithms (PCL) developed in ROS easy to port on UAV PERCEPTION FOR NEO LANDING 12/05/2015 Page 5

6 RELATIVE NAVIGATION /1 Given low 3D sensor resolution, difficult to find robust descriptors. Solution is based on ICP (Iterative Closest Point) between two point clouds (correlation-based approach, considered more robust than feature based approach) ICP flow PERCEPTION FOR NEO LANDING 12/05/2015 Page 6 Alignment of point clouds in simulator

7 RELATIVE NAVIGATION /2 Consecutive point clouds are aligned An overall map is incrementally built to reduce drift In case of registered scans too far apart, the map starts over Y transversal direction Z landing direction PERCEPTION FOR NEO LANDING 12/05/2015 Page 7

8 ABSOLUTE NAVIGATION /1 Need to find a robust descriptor for absolute features Based on VFH (Viewpoint Feature Histogram) Meta-local descriptors, used for object recognition and pose estimation Histogram of angles between viewpoint direction and each normal Training stage with Itokawa model PERCEPTION FOR NEO LANDING 12/05/2015 Page 8

9 ABSOLUTE NAVIGATION /2 Compute VFH descriptor of current point cloud Search for candidates in the training set and select best match in order to get in initial pose Initial posed used for ICP of current point cloud vs map for final pose Training Artificial views generation VFH and pose computation Database storage Implementation VFH computation VFH database matching Initial pose estimation ICP Y Z Final pose estimation X Results along the landing trajectory PERCEPTION FOR NEO LANDING 12/05/2015 Page 9

10 SIMULATION TESTS Simulation tests of navigation in open loop with pre-defined descent trajectories. The following has been tested for 3 scenarios Relative navigation: sensor pose rate error along trajectory Sensor pose error wrt true pose (reference trajectory) Absolute navigation: Sensor pose error along trajectory Quasi-polar landing Quasi-equatorial landing 45deg latitude landing PERCEPTION FOR NEO LANDING 12/05/2015 Page 10

11 SIMULATION TESTS MONTECARLO CAMPAIGN Three Montecarlo campaigns have been carried out to check robustness of algorithms, before going on with experimental tests Manifold of reference trajectories with initial dispersion precalculated Final position errors for absolute navigation +/- 10m +/- 20m +/- 10m Quasi-polar landing Quasi-equatorial landing 45deg latitude landing PERCEPTION FOR NEO LANDING 12/05/2015 Page 11

12 UAV PLATFORM Custom-designed quadcopter ASUS Xtion Pro live used as on-board 3D sensor (similar to Microsoft Kinect): low cost sensor to mimic flashing LIDAR VICON system used for localization Testbed set-up at CATEC PERCEPTION FOR NEO LANDING 12/05/2015 Page 12

13 ITOKAWA MOCKUP USED FOR TESTING The ITOKAWA digital elevation map available from JAXA Hayabusa mission has been used to reproduce a scaled mock-up of the landing site Scale: x45; Fabrication precision: 1 mm Landing site dimensions: 80 m^2 Features have been added in order to enrich the surface, dark grey colour has been added PERCEPTION FOR NEO LANDING 12/05/2015 Page 13

14 HW TEST SETUP 3D NAVIGATION PERCEPTION FOR NEO LANDING 12/05/2015 Page 14

15 EXPERIMENTAL RESULTS DESCENT TRAJECTORY The descent trajectory starts at 150m, ends at 20m (sensor min working distance). Errors in position decrease with height (richer data from sensor) Pose errors stay constant Scaled up to the original scenario we obtain the following results Mean [m] St Dev Position X (m) Position Y (m) Position Z (m) PERCEPTION FOR NEO LANDING 12/05/2015 Page 15

16 EXPERIMENTAL RESULTS HOVERING TRAJECTORY Constant height trajectory, in order to test algorithms to different problems with same mockup. Altitude estimation very stable Good tracking of lateral displacement Pose errors stay constant Scaled up to the real scenario we obtain the following results Mean [m] St Dev Position X (m) Position Y (m) Position Z (m) PERCEPTION FOR NEO LANDING 12/05/2015 Page 16

17 UAV AS VALIDATION PLATFORM FOR SPACE ON-BOARD AUTONOMY GMV has been working on goal oriented on-board autonomy system since several years E4 ESA/ECSS mission autonomy level (see GOAC ESA project presented at ASTRA before) Together with CATEC, we have developed a threelayer controller for UAVs in order to: Further mature and develop a goal-oriented controller Develop planning models for UAV s (based on timelinebased planners EUROPA2) Mature TREX execution layer Test it on a UAV with a robust functional layer (based on ROS) PERCEPTION FOR NEO LANDING 12/05/2015 Page 17

18 ON-BOARD GOAL ORIENTED AUTONOMY Main features of the system: The system has been tested with the goal of performing science On-board resources are continuously checked The planner can react to changes in resources and change its course of action The plan is continuously monitored and adapted Opportunistic science also tested. If a relevant object (rock) is detected, the plan is modified Reacts against unforeseen, hazardouse events (obstacles not present in original map / failure induced in the battery..) Tested indoor in a mars-like environment The following functions are included in the functional layer Path planning Real time obstacle detection and avoidance (sonar altimeter) Target Detection (camera based) PERCEPTION FOR NEO LANDING 12/05/2015 Page 18

19 HW TEST SETUP ONBOARD AUTONOMY PERCEPTION FOR NEO LANDING 12/05/2015 Page 19

20 ARE UAV A GOOD PLATFORM FOR SPACE GNC VALIDATION? UAV has demonstrated being a valid platform for GNC testing for the following reasons: Very robust functional layer Easy transition between simulation and HW The main drawbacks are the following Limited movements and trajectory reproduction (roll/pitch) Limited range Limited control precision (vibration of the UAV), thereby scalability and reproduceability issue Such drawbacks point towards the use of robotic arms for very precise needs, as in navigation tests. PERCEPTION FOR NEO LANDING 12/05/2015 Page 20

21 CONCLUSIONS 3D sensor navigation: Promising results for point cloud estimation in asteroid landing navigation scenario Integration with navigation filter is needed for more in depth assessment Interesting results with UAV tests, although more precise tests could be carried out with robotic arms facilities Autonomous navigation A goal oriented system has been adapted to a robust UAV platform Goal oriented controller further matured UAV are a very interesting validation platform for this application PERCEPTION FOR NEO LANDING 12/05/2015 Page 21

22 Thank you Cristian Chitu (GMV) Giovanni Binet (GMV) Francisco Perez Grau (CATEC) For more information: Property of GMV All rights reserved