Mohsi Jawaid — accepted AI-conference papers

2024

Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation

Mohsi Jawaid, Rajat Talak, Yasir Latif, Luca Carlone, Tat-Jun Chin

IROS 2024poster

Deep learning plays a critical role in vision-based satellite pose estimation. However, the scarcity of real data from the space environment means that deep models need to be trained using synthetic data, which raises the Sim2Real domain gap problem. A major cause of the Sim2Real gap are novel light…

Cited by 1 Source ↗Scholar ↗

2023

Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing

Mohsi Jawaid, Ethan Elms, Yasir Latif, Tat-Jun Chin

ICRA 2023poster

Deep models trained using synthetic data require domain adaptation to bridge the gap between the simulation and target environments. State-of-the-art domain adaptation methods often demand sufficient amounts of (unlabelled) data from the target domain. However, this need is difficult to fulfil when…

Cited by 32 Source ↗Scholar ↗