Abstrakti
The popularity of mobile robots in factories, warehouses, and hospitals has raised safety concerns about human-machine collisions, particularly in non-line-of-sight (NLoS) scenarios such as corners. Developing a robot capable of locating and tracking humans behind the corners will greatly mitigate risk. However, most of them cannot work in complex environments or require a costly infrastructure. This paper introduces a solution that uses the reflected and diffracted Millimeter Wave (mmWave) radio signals to detect and locate targets behind the corner. Central to this solution is a localization convolutional neural network (L-CNN), which takes the angle-delay heatmap of the mmWave sensor as input and infers the potential target position. Furthermore, a Kalman filter is applied after L-CNN to improve the accuracy and robustness of estimated locations. A red-green-blue-depth (RGB-D) camera is attached to themmWave sensor as the annotation system to provide accurate position labels. The results of the experimental evaluation demonstrate that our data-driven approach can achieve remarkable positioning accuracy at the 10-centimeter level without extensive infrastructure. In particular, the approach effectively mitigates the adverse effects of diffraction and multi-bounce phenomena, making the system more resilient.
Alkuperäiskieli | Englanti |
---|---|
Julkaisu | IEEE Sensors Journal |
DOI - pysyväislinkit | |
Tila | E-pub ahead of print - 2024 |
OKM-julkaisutyyppi | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä |
Rahoitus
The work in this paper has been funded by the Academy of Finland within SPHERE-DNA (345681, Secure and Privacy Preserving Healthcare in the Residential Environment with Multimodal Distributed Data and Decentralized AI) project, and Algorithmic Design of 5G Positioning, Sensing and Security Functions (5G-PSS) project through Business Finland (under the grant 6868/31/2021), and is part of the DIOR project that has received funding from the European Union's MSCA RISE programme under grant agreement No. 10100828.
Rahoittajat | Rahoittajan numero |
---|---|
Strategic Research Council at the Research Council of Finland | 345681 |
Business Finland | 6868/31/2021 |
European Union’s Horizon 2020 Research and Innovation Program H2020 MSCA | 10100828what |
Julkaisufoorumi-taso
- Jufo-taso 2
!!ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering