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[Automatic Spatial Calibration of Near-Field MIMO Radar With Respect to Optical Sensors]

Automatic Spatial Calibration of Near-Field MIMO Radar With Respect to Optical Sensors

Vanessa Wirth1,  Johanna Bräunig1,  Danti Khouri1,  Florian Gutsche1,  Martin Vossiek1,  Tim Weyrich1,2,  Marc Stamminger1

1 Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
2 University College London

Abstract

Despite an emerging interest in MIMO radar, the utilization of its complementary strengths in combination with optical depth sensors has so far been limited to far-field applications, due to the challenges that arise from mutual sensor calibration in the near field. In fact, most related approaches in the autonomous industry propose target-based calibration methods using corner reflectors that have proven to be unsuitable for the near field. In contrast, we propose a novel, joint calibration approach for optical RGB-D sensors and MIMO radars that is designed to operate in the radar’s near-field range, within decimeters from the sensors. Our pipeline consists of a bespoke calibration target, allowing for automatic target detection and localization, followed by the spatial calibration of the two sensor coordinate systems through target registration. We validate our approach using two different depth sensing technologies from the optical domain. The experiments show the efficiency and accuracy of our calibration for various target displacements, as well as its robustness of our localization in terms of signal ambiguities.

Citation Style:    Publication

Automatic Spatial Calibration of Near-Field MIMO Radar With Respect to Optical Sensors.
Vanessa Wirth, Johanna Bräunig, Danti Khouri, Florian Gutsche, Martin Vossiek, Tim Weyrich, Marc Stamminger.
Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 8 pages, Abu Dhabi, October 2024. Oral presentation.

Acknowledgments

The authors would like to thank the Rohde & Schwarz GmbH & Co. KG (Munich, Germany) for providing the radar imaging devices. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1483 – Project-ID 442419336, EmpkinS.


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