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Sensor fusion is crucial for autonomous driving and autonomous robots, and meter-wave radar camera fusion systems are popular due to their complementary sensing capabilities. However, accurate calibration between these two sensors is more important to ensure effective fusion and improve overall system performance. The calibration between the two includes internal reference calibration and external reference calibration, the latter is particularly important for achieving accurate sensor fusion. Unfortunately, many target-based calibration methods require complex operating procedures and well-designed experimental conditions, which pose a challenge to researchers. In order to solve this problem, this paper introduces a new method that uses deep learning to extract common features from raw millimeter wave radar data (ie, range Doppler angle data) and camera images. Instead of representing these common features explicitly, our approach implicitly exploits these common features to match the same objects from two data sources, specifically, the extracted common features as-an example, demonstrating an online target-free calibration method between a meter-wave radar and a camera system. In order to improve the accuracy and robustness of calibration, RANSAC and L evenberg-Marquardt (LM) nonlinear optimization algorithms are used to derive the matrix, and the effectiveness and accuracy of the proposed method are proved by experiments in real environment.
Meter-wave radar and cameras are complementary sensing methods that are widely used in applications such as autonomous driving and robotics. Regardless of lighting and weather conditions, the meter wave radar can provide accurate distance, speed and angle information, while the camera can capture high-resolution visual information. The combination of meter-wave radar and camera improves perception and performs tasks such as object recognition, detection and tracking in a dynamic environment. The calibration between the meter-wave radar and the camera is essential for sensor fusion because of their different sensing principles, especially for determining their relative attitude. -- A promising method is to use deep learning to extract useful features from the original millimeter wave radar data, and explore the relationship between radar and image features to estimate the external transformation matrix
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