Efficient human 3D localization and free space segmentation for human-aware mobile robots in warehouse facilities

Dimitrios Arapis*, Milad Jami, Lazaros Nalpantidis

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Real-time prediction of human location combined with the capability to perceive obstacles is crucial for socially-aware navigation in robotics. Our work focuses on localizing humans in the world and predicting the free space around them by incorporating other static and dynamic obstacles. We propose a multi-task learning strategy to handle both tasks, achieving this goal with minimal computational demands. We use a dataset captured in a typical warehouse environment by mounting a perception module consisting of a Jetson Xavier AGX and an Intel L515 LiDAR camera on a MiR100 mobile robot. Our method, which is built upon prior works in the field of human detection and localization demonstrates improved results in difficult cases that are not tackled in other works, such as human instances at a close distance or at the limits of the field of view of the capturing sensor. We further extend this work by using a lightweight network structure and integrating a free space segmentation branch that can independently segment the floor space without any prior maps or 3D data, relying instead on the characteristics of the floor. In conclusion, our method presents a lightweight and efficient solution for predicting human 3D location and segmenting the floor space for low-energy consumption platforms, tested in an industrial environment.

Original languageEnglish
Article number1283322
JournalFrontiers in Robotics and AI
Volume10
Number of pages16
DOIs
Publication statusPublished - 2023

Bibliographical note

Funding Information:
The authors declare financial support was received for the research, authorship, and/or publication of this article. This work has been financially supported by the Novo Nordisk through the industrial Ph.D. project “Reliable Perception for Heterogeneous Mobile Robot Fleets in Intra-factory Logistic Tasks.”

Publisher Copyright:
Copyright © 2023 Arapis, Jami and Nalpantidis.

Keywords

  • Free space segmentation
  • Human localization
  • Mobile robots
  • Multi-task learning
  • Warehouse robotics

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