Self-localization and state estimation are crucial capabilities for agile drone autonomous navigation. This article presents a lightweight and drift-free vision-IMU-GNSS tightly coupled multisensor fusion (LDMF) strategy for drones’ autonomous and safe navigation. The drone is carried out with a front-facing camera to create visual geometric constraints and generate a 3D environmental map. Ulteriorly, a GNSS receiver with multiple constellations support is used to continuously provide pseudo-range, Doppler frequency shift and UTC time pulse signals to the drone navigation system. The proposed multisensor fusion strategy leverages the Kanade–Lucas algorithm to track multiple visual features in each input image. The local graph solution is bounded in a restricted sliding window, which can immensely predigest the computational complexity in factor graph optimization procedures. The drone navigation system can achieve camera-rate performance on a small companion computer. We thoroughly experimented with the LDMF system in both simulated and real-world environments, and the results demonstrate dramatic advantages over the state-of-the-art sensor fusion strategies.
Digital metering system, the electronic energy meter analog-to-digital conversion sampling function moved to the electronic instrument transformer, and electronic instrument transformer sampling frequency compared with the electronic energy meter is low, the sampling frequency will be insufficient in the analog-to-digital conversion process Introduce quantization error. In this paper, the measurement error model of electronic instrument transformer based on analog-to-digital conversion sampling principle is established, and the influence of sampling frequency on energy measurement error is quantitatively analyzed. The analysis shows that the accuracy of electronic instrument transformer measurement is related to oversampling rate.
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