Single-Radar Bilateral 3D Near-Field Imaging for Mobile Robots
Single-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.
The paper proposes a motion-assisted framework using a single forward-mounted millimeter-wave radar to reconstruct 3D structures on both sides of a mobile robot's trajectory, achieving mean lateral-forward localization errors between 4.65 cm and 5.98 cm across office, study, and corridor environments.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Data origin
- Robot trajectoriesOpen source S3
Reported numbers
localization error (office)
4.65
View original evidence
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.
Open source S5localization error (study)
5.63
View original evidence
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.
Open source S5localization error (corridor)
5.98
View original evidence
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.
Open source S5peak-to-background ratio
13
View original evidence
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.
Open source S5
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
perture back-projection coherently focuses the measurements using near-field propagation distances. Top-K power fusion combines local images with different visibility. The reconstruction provides lateral-forward support for multi-layer height extraction, followed by guarded adaptive height refinement using the original MIMO channels. Experiments in controlled, office/study, and corridor environments produce mean late
Open source S4
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
Open source S3
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.
Open source S5
Implications for data suppliers
RoboSignal interpretation and collection questions, not statements of buyer demand.
- Match the buyer’s robot embodiment, action space, sensors and timestamp requirements before planning collection.
- Compare the reported units and scope before using these quantities in a budget. Recording hours, sensor-hours and trajectories are different measures.
- Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.
Source：arXiv Robotics — research abstracts · arxiv.org