5 accepted papers
Dense colored point clouds enhance visual perception and are of significant value in various robotic applications. However, existing learning-based point cloud upsampling methods are constrained by computational resources and batch processing strategies, which often require subdividing point clouds
Adapting pre-trained vision-language models to downstream tasks has emerged as a novel paradigm for zero-shot learning. Existing test-time adaptation (TTA) methods such as TPT attempt to fine-tune visual or textual representations to accommodate downstream tasks but still require expensive optimizat
Highly dynamic maneuvers pose a challenge to conventional state estimators of quadrotors in rapidly tracking the pose. This paper proposes a data-driven Koopman operator-based error-state Kalman filter (K-ESKF) to enhance pose estimation in agile flight. Our method uses the Koopman operator theory t…
Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e.g., left vs. right-hand traffic. In contrast, existing models for autonomous driving have been thus far only deployed within restricted operational domains, \ie,…
UAVs require reliable, cost-efficient onboard flight state estimation that achieves high accuracy and robustness to perturbation. We analyze a multi-sensor extended Kalman filter (EKF) based on the work by Leutenegger. The EKF uses measurements from a MEMS-based inertial system, static and dynamic p…