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:
Current Members |
|||
 |
Ali Raza Undergraduate Researcher Computer Science Specialist alisyed[dot]raza[at]mail.utoronto.ca 
|
 |
Shahmir Akhter Undergraduate Researcher Computer Science & CCIT Double Major shahmir[dot]akhter[at]mail.utoronto.ca
|
 |
Michael Liut Principal Investigator Assistant Professor, Teaching Stream Computer Science michael[dot]liut[at]utoronto.ca
|
||
Past Members |
|||
 |
Lazar Lolic Undergraduate Researcher Computer Science Specialist lazar[dot]lolic[at]utoronto.ca
|
 |
Alfonso Dela Cruz Undergraduate Researcher Computer Science Specialist alfonso[dot]delacruz[at]mail.utoronto.ca
|
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