Predicting human navigation goals based on Bayesian inference and activity regions

Bruckschen, Lilli; Bungert, Kira; Dengler, Nils; Bennewitz, Maren

doi:10.1016/j.robot.2020.103664

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…In the future, we plan to integrate our approach with the work of Bruckschen et al [37] that combines multiple objects to activity regions and uses them for predicting user activities.…”

Section: Run Time Evaluationmentioning

confidence: 99%

Online Object-Oriented Semantic Mapping and Map Updating

Dengler

Zaenker

Verdoja

et al. 2021

2021 European Conference on Mobile Robots (ECMR)

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Creating and maintaining an accurate representation of the environment is an essential capability for every service robot. Especially semantic information is important for household robots acting in indoor environments. In this paper, we present a semantic mapping framework with modular map representations. Our system is capable of online mapping and object updating given object detections from RGB-D data and provides various 2D and 3D representations of the mapped objects. To undo wrong data association, we perform a refinement step when updating object shapes. Furthermore, we maintain a likelihood for each object to deal with false positive and false negative detections and keep the map updated. Our mapping system is highly efficient and achieves a run time of more than 10 Hz. We evaluated our approach in various environments using two different robots, i.e., a HSR by Toyota and a Care-O-Bot-4 by Fraunhofer. As the experimental results demonstrate, our system is able to generate maps that are close to the ground truth and outperforms an existing approach in terms of intersection over union, different distance metrics, and the number of correct object mappings. We plan to publish the code of our system for the final submission.

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“…In the future, we plan to integrate our approach with the work of Bruckschen et al [37] that combines multiple objects to activity regions and uses them for predicting user activities.…”

Section: Run Time Evaluationmentioning

confidence: 99%