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Robust mmWave Radar Sensing With Multisensor Temporal Calibration and Supervision
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DOI：10.1109/JIOT.2026.3668667.png)
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Abstract
En 中文
With the rapid development of Internet of Things (IoT) technologies, autonomous driving has become an integral part of the IoT ecosystem, where millimeter-wave (mmWave) radar plays a crucial role in ensuring robust perception under challenging weather and lighting conditions. However, its sparse and noisy data often require enhancement using high-end sensors such as light detection and ranging (LiDAR) or real-time kinematic global navigation satellite system (RTK-GNSS), which are not common in commercial vehicles. This article introduces mmEMP+, a self-supervised learning technique that leverages pervasive visual–inertial (VI) measurements to enhance radar sensing data. Using VI data to improve radar sensing introduces several challenges. First, moving objects in a scene are inaccurately reconstructed by VI structure-from-motion (SfM), which consequently fails to enhance radar sensing. Second, multipath effects generate spurious radar points that can distort the representation of the environment. Finally, the temporal misalignment between the camera, inertial measurement unit (IMU), and mmWave radar results in mismatched data association, thereby degrading system performance. To address these issues, mmEMP+ first proposes a dynamic 3-D reconstruction method to recover the positions of moving features accurately. Then, we develop a spatial stability checking method to filter out spurious radar points. Finally, mmEMP+ devises a tightly coupled sensor fusion method to calibrate the multisensor temporal offset. Experiments on a real-world dataset show that mmEMP+ achieves performance comparable to high-channel LiDAR-supervised methods while using only low-cost sensors. We further validate its effectiveness in IoT-relevant applications such as object detection, localization, and mapping.
Keywords:
3-D reconstruction
radar point cloud
sensor fusion
temporal calibration
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8.9
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1.4W
Citations:
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