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Autonomous Robots
Erschienen in: Autonomous Robots 4/2021

16.06.2021

Robot-to-robot relative pose estimation using humans as markers

verfasst von: Md Jahidul Islam, Jiawei Mo, Junaed Sattar

Erschienen in: Autonomous Robots | Ausgabe 4/2021

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Abstract

In this paper, we propose a method to determine the 3D relative pose of pairs of communicating robots by using human pose-based key-points as correspondences. We adopt a ‘leader-follower’ framework, where at first, the leader robot visually detects and triangulates the key-points using the state-of-the-art pose detector named OpenPose. Afterward, the follower robots match the corresponding 2D projections on their respective calibrated cameras and find their relative poses by solving the perspective-n-point (PnP) problem. In the proposed method, we design an efficient person re-identification technique for associating the mutually visible humans in the scene. Additionally, we present an iterative optimization algorithm to refine the associated key-points based on their local structural properties in the image space. We demonstrate that these refinement processes are essential to establish accurate key-point correspondences across viewpoints. Furthermore, we evaluate the performance of the proposed relative pose estimation system through several experiments conducted in terrestrial and underwater environments. Finally, we discuss the relevant operational challenges of this approach and analyze its feasibility for multi-robot cooperative systems in human-dominated social settings and feature-deprived environments such as underwater.
Vorheriger Artikel How to train your differentiable filter
Nächster Artikel A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment

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Fußnoten
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Metadaten
Titel
Robot-to-robot relative pose estimation using humans as markers
verfasst von
Md Jahidul Islam
Jiawei Mo
Junaed Sattar
Publikationsdatum
16.06.2021
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 4/2021
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-021-09985-6

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