Jingxue Jiang

I'm Jiang, a recent graduate from Queen's University with a B.A.Sc in Computer Engineering. I'm passionate about robotics and intelligent systems.

Over the past few years, I've had the privilege of contributing to many impactful robotics projects, gaining expertise in ways I never thought possible. Yet, I've come to realize that the value lies not in the achievements themselves, but in the people who have supported me along the way - parents who supported me, mentors who have guided me, and peers who have collaborated with me. Through these relationships, I've learned to approach relationships with humility, persist in facing obstacles with grit, and prioritize others’ needs attentively. I'm grateful for the knowledge gained, but these relationships and experiences have meant more to me than any academic or professional achievement.

This website showcases a curated selection of my project portfolio, encompassing both collaborative endeavours and personal initiatives, so welcome!

Please don't hesitate to reach out to me if you'd like to discuss anything in this portfolio further.

This Python library implements six major nonlinear variants of the Kalman filter targeted at robotics and autonomous system state estimation. It also implements motion simulations based on several kinematic vehicle motion models, including differential drive, bicycle, and Ackermann kinematics.

Localization performance was evaluated using the Multi-Robot Cooperative Localization and Mapping Dataset from the Autonomous Space Robotics Lab (ASRL) at the University of Toronto Institute for Aerospace Studies.

This planner allows robots to probe the farthest reaches of unknown indoor spaces autonomously, finding their way and creating a map without human intervention. The robot builds the map in real-time, and while it explores, it figures out where it wants to go next.

As the project advanced, I recognized an opportunity to apply this planner to floor coverage planning problems. This insight led to the development of a new project—Systematic Floor Coverage of Unknown Environments for Cleaning Robots, which leverages this frontier-based exploration algorithm to achieve comprehensive floor coverage.

Experience