Ultra-wideband (UWB) technology has been widely used for indoor positioning of UAVs due to its excellent range performance. The traditional UWB positioning system requires at least three anchors to complete 3D positioning. Reducing the number of anchors further means reducing the cost and difficulty of deployment. Therefore, this paper proposes a positioning model using only two anchors. In this model, the altitude of the UAV is measured by a rangefinder. Then, the position of the UAV is projected onto the horizontal plane, converting 3D positioning into 2D positioning. The rangefinder’s range accuracy is higher than that of the UWB, which is beneficial for improving 3D positioning accuracy. In addition, an altitude fusion method of integrating rangefinder and barometer data is designed to realize the switching of altitude data and barometer calibration to solve the problem of obstacles under the UAV affecting the altitude measurement. On this basis, the multi-sensor data fusion algorithm based on a dual-anchor positioning model is designed to improve positioning accuracy, and the data of the UWB, rangefinder, barometer, and accelerometer are fused by the unscented Kalman filter (UKF) algorithm. The positioning simulation and experiment show that the positioning accuracy of the dual-anchor model is generally higher than that of the three-anchor model, with decimeter-level positioning accuracy.

Accurate altitude estimation is critical for unmanned aerial vehicles (UAVs), yet barometric measurements are susceptible to atmospheric drift and dynamic disturbances. To address these limitations, this paper proposes a dual-layer, real-time differential barometric altimetry framework that integrates a ground reference station with an onboard fusion scheme based on Adaptive Weighted Averaging (AWA) and an Adaptive Extended Information Filter (AEIF). The ground reference station suppresses low-frequency atmospheric variations, while the onboard AEIF incorporates a physical pressure–height model and adaptive noise estimation to maintain a fast dynamic response. The proposed method is validated through numerical simulations, hardware-in-the-loop (HIL) experiments, and real flight tests. In a two-hour outdoor flight test, compared with barometric systems operating without a reference station, the proposed approach reduces the altitude RMSE from 4.05 m to 0.31 m, achieving an approximately order-of-magnitude improvement in representative scenarios and demonstrating decimeter-level altitude measurement accuracy.

Unmanned aerial vehicles (UAVs) are increasingly used in civilian and military applications, where accurate altitude estimation is critical for safe and reliable flight. In GPS-denied indoor environments, single sensors such as barometers or laser rangefinders often suffer from noise, drift, and sudden measurement faults, limiting their reliability. This paper proposes a multi-sensor altitude fusion algorithm based on an adaptive Kalman filter that integrates data from a barometer, laser rangefinder, and accelerometer. A barometer bias term is included in the state-space model for continuous calibration, while a residual-driven adaptive mechanism dynamically tunes process and measurement noise to suppress laser anomalies. Simulation results show that the proposed method improves altitude estimation accuracy and maintains robustness during sensor disturbances, making it suitable for UAV indoor navigation and control tasks.

With the acceleration of urbanization, the amount of household waste generated has increased sharply. In order to solve this problem and promote the intelligent upgrade of urban garbage disposal, a garbage sorting system combining Rasp¬berry Pi and robotic arm was designed. The hardware part of the system includes components such as Raspberry Pi main control board, robotic arm extension board, six degree of freedom robotic arm, USB camera, etc. The software utilizes platforms such as VNC and Jupyter Lab to train a suitable model by improving the Yolov4-tiny algorithm, which is then ported to the Raspberry Pi. The camera module detects the category of garbage, and the Raspberry Pi main control board controls the movement of the robotic arm to classify the garbage and send it to the correct location. The test results show that the average recognition success rates of the system under strong light, weak light, and normal light conditions are 92.00%, 93.00%, and 96.50%, respectively.