Demonstration of a Stereo Visual Odometry Algorithm

I’m pleased to share another demonstration video of our stereo visual odometry algorithm, primarily developed by my student Mohamed Aladem who is wrapping up his master’s at the University of Michigan – Dearborn. Near term goals for our lab using this framework are: navigating mobile robots (namely an autonomous snowplow for the ION Autonomous Snowplow Competition – see previous post to this one), navigating a multi-copter, and explore solutions for automotive driver assistance systems and future autonomous vehicles.


  • Mohamed Aladem, Samir Rawashdeh, Nathir Rawashdeh, “Evaluation of a Stereo Visual Odometry Algorithm for Road Vehicle Navigation”, SAE World Congress, April 2017 Detroit, MI
  • S. A. Rawashdeh, M. Aladem, “Toward Autonomous Stereo-Vision Control of Micro Aerial Vehicles”, Proceedings of the IEEE National Aerospace and Electronics Conference, July 2016, Dayton, OH
  • Journal article pending.


2017 Autonomous Snowplow Competition

At the 2017 ION Autonomous Snowplow Competition, of 8 competing teams UM-Dearborn’s Yeti won second place ($4000) and team Zenith won first place in the new Cooperative Snowplow challenge ($700). 
Yeti and Zenith are primarily developed by students from the Intelligent Systems Club (ISC), advised by prof. Rawashdeh. Yeti uses a LIDAR for localization and obstacle avoidance, while Zenith is based on stereo vision. 
Some photos and videos can be found on the ISC club’s Twitter page.