9 papers
NeMo-map: Neural Implicit Flow Fields for Spatio-Temporal Motion Mapping
Yufei Zhu, Shih-Min Yang, Andrey Rudenko +3
Safe and efficient robot operation in complex human environments can benefit from good models of site-specific motion patterns. Maps of Dynamics (MoDs) provide such models by encod…
Trajectory prediction for heterogeneous agents: A performance analysis on small and imbalanced datasets
Tiago Rodrigues de Almeida, Yufei Zhu, Andrey Rudenko +4
Robots and other intelligent systems navigating in complex dynamic environments should predict future actions and intentions of surrounding agents to reach their goals efficiently…
Long-Term Human Motion Prediction Using Spatio-Temporal Maps of Dynamics
Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner +2
Long-term human motion prediction (LHMP) is important for the safe and efficient operation of autonomous robots and vehicles in environments shared with humans. Accurate prediction…
Collecting Human Motion Data in Large and Occlusion-Prone Environments using Ultra-Wideband Localization
Janik Kaden, Maximilian Hilger, Tim Schreiter +5
With robots increasingly integrating into human environments, understanding and predicting human motion is essential for safe and efficient interactions. Modern human motion and ac…
Large-scale visual SLAM for in-the-wild videos
Shuo Sun, Torsten Sattler, Malcolm Mielle +2
Accurate and robust 3D scene reconstruction from casual, in-the-wild videos can significantly simplify robot deployment to new environments. However, reliable camera pose estimatio…
Fast Online Learning of CLiFF-maps in Changing Environments
Yufei Zhu, Andrey Rudenko, Luigi Palmieri +3
Maps of dynamics are effective representations of motion patterns learned from prior observations, with recent research demonstrating their ability to enhance various downstream ta…