Project Topics



  • Presentation (10%)
  • Course Notes (10%)
  • Demo/Instructional/Exercise (20%)

Note: If you were a group of two then you do not need to complete the Course Notes - instead the Presentation is 15% and the Demo/Instructional/Exercise is 25%.


Make a ~30min presentation to the class about your topic. The rest of us will fill out this feedback form during your presentation.

Course Notes

Summarize the background material related to your topic in the format of the textbook. The process for doing this is documented here. In the book we use the “Markduck” format - which is basically markdown with some extras. For example it can parse LaTeX and add figures and many other things. For a full description have a look here.

Please add your notes to the “Learning Materials” (i.e. you should fork this repo).


You should produce something that we can test and play with for ourselves. This can be:

  • A demo that runs on the robot
  • A Collab notebook that we can follow and learn something
  • An exercise that we follow and learn something

The instructional can use the simulator, the logs, or anything else you would like to use.

You need to document what you produce instructions for us to follow. This should be in “Markduck” format (see above).

If you have built a robot demo, please add your instructions to the demo section here (i.e., you should fork this repo).

If you have made an instructional or exercise, please add your instructions to the “Exercises” book (i.e. you should fork this repo).

Project Topics

Please fill out the form. Deadline to fill it out is end of day Wednesday Sept 19th otherwise you will be randomly allocated.

Semantic segmentation


Team: Philippe Lacaille and Samuel Lavoie

Possibly representative/interesting papers coud include:

  • SegNet and papers that ensued

Visual (intertial) odometry / optical flow


Team: Tien Nguyen, Pravish Sainath, and Iban Harlouchet

Possibly representative/interesting papers could include:

Object detection and tracking


Team: Jonathan Plante, Ruixiang Zhang, Orlando Marquez, and Yun Chen

Possibly representative/interesting papers could include:

Planning with uncertainty

Team: Martin Weiss, Vincent Mai, and Gunshi Gupta

Maps and representations


No Team

Simultaneous localization and mapping

Team: Krishna Murthy, Benjamin Ramtoula, Mandana Samiei

  • Possibly representative/interesting papers could include:

Free space estimation


Team: Laurent Mandrile, and David Abraham

  • Possibly representative/interesting papers could include:

Automated calibration


No Team

Testing and Validation


No Team

Fleet-level Planning and Ride Sharing

No Team

Advanced Planning and Control

No Team