Fusion Ranging Method of Monocular Camera and Millimeter-Wave Radar Based on Improved Extended Kalman Filtering.

To address the limitations of single-sensor systems in environmental perception, such as the difficulty in comprehensively capturing complex environmental information and insufficient detection accuracy and robustness in dynamic environments, this study proposes a distance measurement method based on the fusion of millimeter-wave (MMW) radar and monocular camera. Initially, a monocular ranging model was constructed based on object detection algorithms. Subsequently, the pixel-distance joint dual-constraint matching algorithm is employed to accomplish cross-modal matching between the MMW radar and the monocular camera. Furthermore, an adaptive fuzzy extended Kalman filter (AFEKF) algorithm was established to fuse the ranging data acquired from the monocular camera and MMW radar. Experimental results demonstrate that the AFEKF algorithm achieved an average root mean square error (RMSE) of 0.2131 m across 15 test datasets. Compared to the raw MMW radar data, inverse variance weighting (IVW) filtering, and traditional extended Kalman filter (EKF), the AFEKF algorithm improved the average RMSE by 10.54%, 11.10%, and 22.57%, respectively. The AFEKF algorithm improves the extended Kalman filter by integrating an adaptive fuzzy mechanism, providing a reliable and effective solution for enhancing localization accuracy and system stability.