Dynamic SLAM system for hospital logistics robots based on nonlinear optimal filtering and deep learning

The existing SLAM (Simultaneous Localization and Mapping) systems often suffer from increased positioning errors, inaccurate map construction, and challenges in real-time multimodal sensor data fusion in dynamic environments. This paper proposes enhancements to the SLAM system using nonlinear optimal filtering and deep learning to improve adaptability in such conditions. The study employs an Unscented Kalman Filter (UKF) for nonlinear state estimation, while deep feature extraction of environmental images is conducted via Convolutional Neural Networks (CNN). Semantic edge detection integrates Fully Convolutional Networks (FCN) and Canny edge detection techniques. The Extended Kalman Filter (EKF) is utilized for multimodal data fusion to optimize positioning accuracy across vision, lidar, and inertial measurement unit (IMU) sensors. Real-time motion estimation is achieved through an event-based camera combined with an optical flow algorithm, enhancing speed and accuracy in dynamic scenes. Experimental results demonstrate that the proposed SLAM system achieves an absolute trajectory error (ATE) as low as 0.067 m across datasets, with over 90% overlap in constructed maps. The system's average frame processing time is under 90 ms, and it responds within an average of 0.035 s during event-based camera experiments. These results outperform other mainstream SLAM systems, confirming that nonlinear optimal filtering and deep learning significantly enhance positioning accuracy, mapping quality, and real-time performance in complex environments. This study primarily employs simulation-based experiments to validate the proposed SLAM system's performance in dynamic hospital environments. While the results demonstrate high accuracy (e.g., an absolute trajectory error of 0.07 m), future work will include field experiments to further verify the system's robustness in real-world applications.