Mr. Sami Ullah Kashif is a lecturer at CASE in the Department of Computer Science.

• Masters in Computer Science with focus on Computer Vision
• Bachelors in Computer Science with minor in Mathematics

• Worked as an RA on the project named “Activity Recognition in Videos”


• Joint project between LUMS and University of Illinois, Chicago
• Funding provided by National Science Foundation (NSF), USA

Awarded prestigious Fulbright Scholarship for the Masters degree

1. Simultaneous localization and mapping (SLAM) is a method that robots use to explore, navigate, and map an unknown environment while at the same time keeping track of their current location. To lower computation costs associated with SLAM, smoothing and mapping (SAM) approaches are used in real scenarios. However, in SAM approaches, linearization of non-linear variables is still the most time consuming step. To mitigate this problem, a submap-based approach, Tectonic SAM, is used in which the original optimization problem is divided into submaps which can be optimized independently and parameterized relative to a local coordinate frame. This approach allows the problem to be solved in a parallel and distributed manner.


2. The goal of the project is to keep track of number of empty slots in the parking area. This system is based on the techniques of Computer Vision and is an alternative to the traditional approach of using sensors. The system is tolerant to changes in illumination and can work even if the camera is looking at the cars from the oblique angle