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KI - Künstliche Intelligenz
Erschienen in: KI - Künstliche Intelligenz 2/2020

27.05.2020 | Technical Contribution

ITP: Inverse Trajectory Planning for Human Pose Prediction

verfasst von: Pedro A. Peña, Ubbo Visser

Erschienen in: KI - Künstliche Intelligenz | Ausgabe 2/2020

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Abstract

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.
Vorheriger Artikel How and What Can Humans Learn from Being in the Loop?
Nächster Artikel Mutual Explanations for Cooperative Decision Making in Medicine

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Metadaten
Titel
ITP: Inverse Trajectory Planning for Human Pose Prediction
verfasst von
Pedro A. Peña
Ubbo Visser
Publikationsdatum
27.05.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
KI - Künstliche Intelligenz / Ausgabe 2/2020
Print ISSN: 0933-1875
Elektronische ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-020-00658-7

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