Indoor Positioning & Computer Vision (IPCV) Research Group

About Us

We are a research group from the University of Toronto, Mississauga Mathematical and Computational Sciences department. Our current research interests lie in indoor and outdoor positioning, localization and navigation that can be applied to mobile robots, drones, and AR/VR systems. In the past we have compared and evaluated multiple current indoor positioning techniques and further developed and improved specific methods, including computer vision and AI/ML techniques. We are also currently researching and exploring sensor fusion and IMU-based positioning.

Research Interests:

  • Sensor Fusion and filtering
  • Deep Learning for Computer Vision
  • Inertial Navigation Systems
  • Simultaneous Localization and Mapping
  • Object and Camera Pose Estimation
  • Perception for Autonomous Vehicles
GitHub Page


Current Members

Ali Raza
Undergraduate Researcher
Computer Science Specialist
Shahmir Akhter
Undergraduate Researcher
Computer Science & CCIT Double Major
Michael Liut
Principal Investigator
Assistant Professor, Teaching Stream
Computer Science

Past Members

Lazar Lolic
Undergraduate Researcher
Computer Science Specialist
Alfonso Dela Cruz
Undergraduate Researcher
Computer Science Specialist


Comparing and Evaluating Indoor Positioning Techniques.

Ali Raza, Lazar Lolic, Shahmir Akhter, Michael Liut.

2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021), Lloret de Mar, Spain, November 2021.


Deep Camera Pose Regression Using Pseudo-LiDAR (Pre-print)

Ali Raza, Lazar Lolic, Shahmir Akhter, Alfonso Dela Cruz, Michael Liut.


This research was supported by the National Sciences and Research Council of Canada (NSERC) grant USRA/567510-2021, the University of Toronto Mississauga's (UTM) Office of the Vice-Principal Research (OVPR) Fund, Dean's Office Research Opportunity Project (ROP) Fund, and UTM's Department of Mathematical and Computational Sciences