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My research objective is to enable autonomous intelligent systems to efficiently and safely operate in the noisy and dynamic reality of shared human-robot environments. My conviction is that to achieve this goal it is essential to enable robots to acquire high-level knowledge through implicit ques of daily human behavior and through enabling robots to continuously self-assess and evaluate the quality of the said models.
Investigates recent methods that make it possible to represent the broad range of real-world spatial motion patterns in a compact, yet meaningful way. Primarily focuses on creating maps that capture the motion patterns of dynamic objects and/or the flows of continuous media.
Presents recent research on probabilistic mapping of motion patterns for mobile robots.
Recorded data includes motion capture files as Matlab structures and csv files, velodyne sweeps, ros bags from the motion capture system. We also share the Matlab scripts for loading, plotting and animating the motion capture data. We thoroughly inspected the motion capture data and manually cleaned it to remove occasional helmet ID switches and recover several lost tracks. Afterwards we applied an automated procedure to restore the lost positions of the helmets from incomplete set of recognized markers (included as a Matlab script).
Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots
Tracking Human Motion at Örebro University