Single-Radar Bilateral 3D Near-Field Imaging for Mobile Robots
Overview
This research presents a motion-assisted framework that enables mobile robots to reconstruct 3D structures on both sides of their trajectory using a single forward-mounted millimeter-wave radar. The method addresses challenges such as near-field wavefront curvature and limited elevation diversity through spherical path modeling, bilateral transmitter combining, sliding-aperture back-projection, and Top-K power fusion. Experiments in controlled, office/study, and corridor environments demonstrate mean lateral-forward localization errors of 4.65 cm, 5.63 cm, and 5.98 cm respectively, with peak-to-background ratios exceeding 13 dB. The approach recovers bilateral 3D scattering structures from a single forward pass without relying on learned scene representations.
Generated from attributed reports · Updated 10 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Data origin: robot trajectories
“presents a motion-assisted framework for reconstructing structures on both sides of a robot's trajectory. Our near-field model relates scene reflectivity to stepped-frequency multiple-input multiple-output (MIMO) observations across frequencies and robot positions using spherical paths from transmitter (Tx) to scatterer to receiver (Rx). Bilateral Tx combining provides wideband directional weighting, while sliding-a”
Exact source · revision 1Source owner not reported
Reported quantity · localization error (office): 4.65 other · Basis not reported
“ral-forward localization errors of 4.65, 5.63, and 5.98 cm, respectively, with peak-to-background ratios (PBRs) exceeding 13 dB. The framework recovers bilateral 3D scattering structure from one forward pass using a single radar, without relying on learned representations of the scene.”
Exact source · revision 1Source owner not reported
Reported quantity · localization error (study): 5.63 other · Basis not reported
“ral-forward localization errors of 4.65, 5.63, and 5.98 cm, respectively, with peak-to-background ratios (PBRs) exceeding 13 dB. The framework recovers bilateral 3D scattering structure from one forward pass using a single radar, without relying on learned representations of the scene.”
Exact source · revision 1Source owner not reported
Reported quantity · localization error (corridor): 5.98 other · Basis not reported
“ral-forward localization errors of 4.65, 5.63, and 5.98 cm, respectively, with peak-to-background ratios (PBRs) exceeding 13 dB. The framework recovers bilateral 3D scattering structure from one forward pass using a single radar, without relying on learned representations of the scene.”
Exact source · revision 1Source owner not reported
Reported quantity · peak-to-background ratio: 13 other · Basis not reported
“ral-forward localization errors of 4.65, 5.63, and 5.98 cm, respectively, with peak-to-background ratios (PBRs) exceeding 13 dB. The framework recovers bilateral 3D scattering structure from one forward pass using a single radar, without relying on learned representations of the scene.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSingle-Radar Bilateral 3D Near-Field Imaging for Mobile Robots
This research presents a motion-assisted framework that enables mobile robots to reconstruct 3D structures on both sides of their trajectory using a single forward-mounted millimeter-wave radar. The method addresses challenges such as near-field wavefront curvature and limited elevation diversity through spherical path modeling, bilateral transmitter combining, sliding-aperture back-projection, and Top-K power fusion. Experiments in controlled, office/study, and corridor environments demonstrate mean lateral-forward localization errors of 4.65 cm, 5.63 cm, and 5.98 cm respectively, with peak-to-background ratios exceeding 13 dB. The approach recovers bilateral 3D scattering structures from a single forward pass without relying on learned scene representations.
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