Tracking and predicting humans in three dimensional space in order to know the location and heading of the human in the environment is a difficult task. Though if solved it will allow a robotic agent to know where it can safely be and navigate the environment without imposing any danger to the human that it is interacting with. We propose a novel probabilistic framework for robotic systems in which multiple models can be fused into a circular probabilitymap to forecast human poses. We developed and implemented the framework and tested it on Toyota’s HSR robot and Waymo Open Dataset. Our experiments show promising results.
ASJC Scopus subject areas
- Artificial Intelligence