Robohub.org
 

Driving, flying, and climbing in a sand and gravel pit


by
18 July 2016



share this:
NCFRN Field Trials 2016. Photo: Tim Barfoot

NCFRN Field Trials 2016. Grizzly in challenging lighting. Photo: Tim Barfoot

In June, the University of Toronto (as part of the NSERC Canadian Field Robotics Network) carried out a set of field trials at an old sand and gravel pit in Sudbury, Ontario, Canada. This involved three main experiments:

  1. A new version of our Visual Teach and Repeat (VT&R) approach to vision-only route following,
  2. Aerial surveys of the site using fixed-wing unmanned aerial vehicles, and
  3. A tethered robot design for mapping steep surfaces such as cliffs.

In the VT&R experiments, we taught our robot a 5 km network of interconnected paths, then carried out 120 km of autonomous repeats on these paths using only stereo vision for feedback. The below video shows some sections being repeated. Our new technique, dubbed VT&R 2.0, is a significant advance over our earlier work in that (i) it uses a Multi­-Experience Localization (MEL) technique to match live images to several previous experience of a path (making it more robust to appearance change), and (ii) is able to do place-­dependent terrain assessment to safeguard the robot and people around it, even in rough terrain with vegetation.

The team also captured some great images of the various experiments.  All images copyright Tim Barfoot or Francois Pomerleau.

grizzly2 grizzly3 grizzly4 grizzly5

All photos can be viewed here.


This video is associated with the following papers:

Paton M, MacTavish K A, Warren M, and Barfoot T D. “Bridging the Appearance Gap: Multi-Experience Localization for Long-Term Visual Teach and Repeat”. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, 9-14 October 2016

Berczi L P and Barfoot T D. “It’s Like Déjà Vu All Over Again: Learning Place-Dependent Terrain Assessment for Visual Teach and Repeat”. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, 9-14 October 2016



tags:


Tim Barfoot Dr. Timothy Barfoot (Professor, University of Toronto Institute for Aerospace Studies -- UTIAS) holds the Canada Research Chair (Tier II) in Autonomous Space Robotics and works in the area of guidance, navigation, and control of mobile robots for space and terrestrial applications.
Tim Barfoot Dr. Timothy Barfoot (Professor, University of Toronto Institute for Aerospace Studies -- UTIAS) holds the Canada Research Chair (Tier II) in Autonomous Space Robotics and works in the area of guidance, navigation, and control of mobile robots for space and terrestrial applications.

            AUAI is supported by:



Subscribe to Robohub newsletter on substack



Related posts :

Robot Talk Episode 153 – Origami-inspired robots, with Chenying Liu

  24 Apr 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Chenying Liu from University of Oxford about how a robot's physical form can actively contribute to sensing, processing, decision-making, and movement.

Sony AI table tennis robot outplays elite human players

  22 Apr 2026
New robot and AI system has beaten professional and elite table tennis players.

AI system learns to keep warehouse robot traffic running smoothly

  20 Apr 2026
This new approach adapts to decide which robots should get the right of way at every moment, avoiding congestion and increasing throughput.

Robot Talk Episode 152 – Dexterous robot hands, with Rich Walker

  17 Apr 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Rich Walker from Shadow Robot Company about their advanced robotic hands for research and industry.

What I’ve learned from 25 years of automated science, and what the future holds: an interview with Ross King

and   14 Apr 2026
Ross King created the first robot scientist back in 2009. He spoke to us about the nature of scientific discovery, the role AI has to play, and his recent work in DNA computing.

Robot Talk Episode 151 – Robots to study the ocean, with Simona Aracri

  10 Apr 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Simona Aracri from National Research Council of Italy about innovative robot designs for oceanography and environmental monitoring.

Generative AI improves a wireless vision system that sees through obstructions

  08 Apr 2026
With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.

Resource-constrained image generation and visual understanding: an interview with Aniket Roy

  07 Apr 2026
Aniket tells us about his research exploring how modern generative models can be adapted to operate efficiently while maintaining strong performance.



AUAI is supported by:







Subscribe to Robohub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence