Simultaneous Localization and Mapping (SLAM) is one of the key technologies with which to address the autonomous navigation of mobile robots, utilizing environmental features to determine a robot’s position and create a map of its surroundings. Currently, visual SLAM algorithms typically yield precise and dependable outcomes in static environments, and many algorithms opt to filter out the feature points in dynamic regions. However, when there is an increase in the number of dynamic objects within the camera’s view, this approach might result in decreased accuracy or tracking failures. Therefore, this study proposes a solution called YPL-SLAM based on ORB-SLAM2. The solution adds a target recognition and region segmentation module to determine the dynamic region, potential dynamic region, and static region; determines the state of the potential dynamic region using the RANSAC method with polar geometric constraints; and removes the dynamic feature points. It then extracts the line features of the non-dynamic region and finally performs the point–line fusion optimization process using a weighted fusion strategy, considering the image dynamic score and the number of successful feature point–line matches, thus ensuring the system’s robustness and accuracy. A large number of experiments have been conducted using the publicly available TUM dataset to compare YPL-SLAM with globally leading SLAM algorithms. The results demonstrate that the new algorithm surpasses ORB-SLAM2 in terms of accuracy (with a maximum improvement of 96.1%) while also exhibiting a significantly enhanced operating speed compared to Dyna-SLAM.

The estimation accuracy of the battery state of charge (SOC) directly affects the reliable and stable operation of the battery management system (BMS). In complex operating conditions such as vehicle-mounted applications and energy storage power stations, systematic biases and random noise are common in voltage and current sensing measurement. Most existing studies on SOC error focus on the independent effect of individual measurement error and lack systematic explanation on the propagation mechanism and quantitative influence of multi-source errors. In this paper, based on the second-order RC equivalent circuit model, the analytical transfer model of SOC estimation error is established under the framework of the extended Kalman filter (EKF), and the transfer path of measurement error in the algorithm is clarified. Then, a battery test bench with “reference-measurement” dual-channel parallel measurement is built. Under the constant-current discharge condition, the contribution weight quantification and sensitivity analysis of multi-source measurement error to SOC estimation error are completed, and it is revealed that the voltage measurement bias is the dominant error source of SOC estimation. On this basis, an augmented-state modified EKF algorithm (AM-EKF) with online identification of voltage bias is proposed. The voltage measurement bias is incorporated into the state vector to realize the joint estimation and online compensation of the operating state and measurement bias. The results show that under the constant-current discharge condition, the improved algorithm increases the proportion of SOC estimation error within the ±3% qualified interval to more than 85% in the whole discharge period. The maximum estimation error is significantly lower than that of the traditional EKF algorithm, and the accuracy is close to the unbiased ideal condition. Under the modified DST dynamic condition, the improved algorithm controls the SOC estimation error within 5%, which is 72% lower than the traditional EKF calculation error with bias input. The scheme has high robustness, no additional hardware cost, controllable computational complexity and excellent engineering application value.