Challenge 3. Markus Achtelik. ETH Zurich.

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1 Challenge 3 Markus Achtelik ETH Zurich markus.achtelik@mavt.ethz.ch

2 Challenge 3 Motivation and RTD Issues Plant Servicing and Inspection with Aerial Robots Inspect environments, that are dangerous, tedious and / or difficult to reach for humans (semi-) autonomous operation to assist inspection experts without piloting skills General assumptions No / restricted GPS, Indoor & outdoor operation real 3D tasks, no 2.5 D assumptions Research topics Robust perception in challenging environments Multi-sensor fusion Path / task planning Interaction with operator 2

3 3 Example Scenario

4 4 Example Scenario

5 Challenge 3 Platform Revised and enhanced version of the sfly (EU-FP7) hex-rotor helicopter AscTec Firefly Optimized for size and weight safety flight time Sufficient payload capability for On-board computers VI-sensor including up to four cameras Challenger / end user payloads Coupling of VI-sensor with on-board flight control and IMU 5

6 Challenge 3 Platform Visual-Inertial SLAM sensor Vision: Global Shutter Aptina MT9V034 (up to four) IMU: Analog Devices ADIS 16488/16448 Calibration: Camera-IMU fully calibrated & time-synchronized FPGA: XILINX Zynq 7020 SoC Dual-Core ARM Cortex A9 Lighting: LED flasher I/O: GigE, USB-powered (<10W) Weight: 130 g (incl. 2 cams + sensor mount) 6

7 Challenge 3 Platform low-level control, safety features user code 7

8 Challenge 3 Support Challenge Host: Autonomous Systems Lab and Institute for Dynamic Systems and Control (ETHZ) Help with software integration, building blocks for MAV operation / navigation Onsite support for Realistic Labs Simulation environment throughout the challenge Technology Supplier: Ascending Technologies (ASC) General MAV platform support Help with electrical/mechanical integration of on-board sensors and electronics MAV / Autopilot interfaces System Integrator: Alstom Inspection Robotics (AIR) Advice and support for the proposed industrial use-cases Supporting the relationship to the end-user Practical support for onsite demonstrations 8

9 Challenge 3 Stage 1: Qualifying Two separate tracks, either for experts in one field or all-rounders Track 1: Vision based localization and reconstruction Realistic datasets with increasing difficulty Localization and reconstruction from stereo images and IMU Data Focus on local consistency and speed Track 2: State estimation, control and navigation State estimation with IMU and with noisy/slow/delayed 6-DoF pose measurements from simulated sensors Position control, disturbance rejection, waypoint control Waypoint planning 9

10 Challenge 3 Stage 1: Qualifying Impressions Task 1.3: Localization from stereo images and IMU, difficult dataset 10

11 Challenge 3 Stage 1: Qualifying Impressions Task 4.3: Navigation in an industrial environment 11

12 Challenge 3 Stage 1: Qualifying Impressions Teams are very active and motivated; some uploaded every few minutes to the online evaluation More than 4000 evaluation uploads Most teams are above minimum expectations Top 5 teams have points from 120 possible 12

13 Challenge 3 Stage 2: Realistic Labs Collision free navigation in a complex GPS-restricted industrial environment. Task allocation by human operator (no trained pilot) using on board camera footage or direct view. Benchmarking task TBD by ETHZ / ASC / AIR based on use cases submitted Same setup for all teams Freestyle task TBD by challengers Showcase task TBD by end user 13

14 Main tasks: Challenge 3 Stage 2: Realistic Labs 3-D scenario Localization and reconstruction (local) Handling of multiple sensors Collision avoidance, also dynamic and small (e. g. hanging cables!) Path-planning Accurate 3-D reconstruction 14

15 Challenge 3 Stage 2: Realistic Labs Access to MAV-platform: MAV-platform including VI-Sensor for each challenger team provided during Stage 2 (3) Platform setup workshop at ETH Hardware in the loop simulation planned Flying arena: Located at ETH Realistic mock-up, also included in simulation Accurate ground truth Vicon safety-pilot Testing time scheduled with challenger teams 15

16 Challenge 3 Stage 3 End-user site depends on match-making and outcome of stage 2. Refine and advance results of stage 2 for testing in a real-world environment: Automatic following of structures (wall, pipes) Passage through narrow areas Automated homing Fast maneuvers Optimized Path planning strategies 16

17 Challenge 3 Contact ETHZ: Markus Achtelik, markus.achtelik@mavt.ethz.ch ASC: Daniel Gurdan, daniel.gurdan@asctec.de AIR: Ekkehard Zwicker, ekkehard.zwicker@inspection-robotics.com 17

18 Challenge 3 Markus Achtelik ETH Zurich markus.achtelik@mavt.ethz.ch