Prof. Margarita Chli gave a stunning introduction about her work about robots and artificial intelligence at our Zurich meetup in September 2018.

1. Margarita Chli
Vision for Robotics Lab, ETH Zurich
www.v4rl.ethz.ch
Teaching Robots to See

2. Copyright Warren Sanders

3. sky
clouds
monument
motorist
sunny trees
people
roundabout
statue
bus
car
street
shadow

4. CROP MONITORING INSPECTION of INFRASTRUCTURE
DIGITIZATION IN ARCHAEOLOGY SEARCH & RESCUE
Computer
Vision
&
Robotics

5. Unpacking the Idea(see notes for details)
[IROS 2012] with M. Achtelik, S. Lynen, S. Weiss, L. Kneip and R. Siegwart

6. Teaching Robots to See – are we there yet?
Handle larger amounts of data more effectively
§ Competing goals:
Precision vs. Efficiency
• Fast motion
• Large scales
• Rich maps
• Robustness
• Lower Computation
1ms Target Tracking Vision Chip by Ishikawa
Komuro University
Photo credits: Max Pixel

7. Weight
ü Lighter & safer than larger robots
✗ Limited resources: sensors, computation, …
Agility
ü Very agile, fast
✗ Fast, unstable dynamics, cannot “stop”
Autonomy
✗ Battery, communication bandwidth
Teaching Drones to See – properties & challenges
Photo credit: Francois Pomerleau

8. Develop
collaborative
visual perception & intelligence
The Vision

9. Develop
collaborative
visual perception & intelligence
Extension to other platforms
The Vision

10. 1. Robust localization & mapping
?
1

11. … e.g. via sensor fusion
[ICRA 2018]: with R. Mascaro, L. Teixeira, T.
Hinzmann and R. Siegwart
?
1

12. Localization & Mapping downtown Zurich
Estimated Trajectory
Estimated Map
Current Map points
Place Recognition
• Recognize when the robot visits a
place it has seen before.
• Search for matching images across
robot’s trajectory.
Camera view
?
1

13. Place Recognition
Different places can appear identical
Large viewpoint changes (especially from an aircraft)
Seasonal / Illumination changes
Place appearance changes between visits

14. [3DV 2017] with F. Maffra,
L. Teixeira and Z. Chen
Place Recognition using orthophotos
Current view
Current Orthophoto
Database match: Original view
Database match: Orthophoto
?
1

15. 1
?
Current View Database match
[IROS 2017]: with Z. Chen, F. Maffra and I. Sa
Place Recognition using deep learning

16. 2
1
?
2. Scene reconstruction
[IROS 2016] with M. Karrer,
M. Kamel and R. Siegwart

17. … e.g. to avoid obstacles
2
1
? [ICRA 2017] with L. Teixeira

18. Camera view
Dense mapping for collision-free path planning
2
1
?
[FSR 2017] with L. Teixeira, and I. Alzugaray

19. 3. Robot Collaboration
2
1
?
3
[ICRA 2017] with P. Schmuck

20. with P. Schmuck and M. Karrer

21. Robotic Perception & Collaboration are key to Robotic Autonomy
2
1
?
3
Photo credit: Mario Trimikliniotis

22. 2
1
?
3