Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments.

This work presents a novel RGB-D dynamic Simultaneous Localisation and Mapping (SLAM) method that improves the precision, stability, and efficiency of localisation while relying on lightweight deep learning in a dynamic environment compared to the traditional static feature-based visual SLAM algorithm. Based on ORB-SLAM3, the GCNv2-tiny network instead of the ORB method, improves the reliability of feature extraction and matching and the accuracy of position estimation; then, the semantic segmentation thread employs the lightweight YOLOv5s object detection algorithm based on the GSConv network combined with a depth image to determine potentially dynamic regions of the image. Finally, to guarantee that the static feature points are used for position estimation, dynamic probability is employed to determine the true dynamic feature points based on the optical flow, semantic labels, and the state in last frame. We have performed experiments on the TUM datasets to verify the feasibility of the algorithm. Compared with the classical dynamic visual SLAM algorithm, the experimental results demonstrate that the absolute trajectory error is greatly reduced in dynamic environments, and that the computing efficiency is improved by 31.54% compared with the real-time dynamic visual SLAM algorithm with close accuracy, demonstrating the superiority of DLD-SLAM in accuracy, stability, and efficiency.

Visual–inertial SLAM algorithms empower robots to autonomously explore and navigate unknown scenes. However, most existing SLAM systems heavily rely on the assumption of static environments, making them ineffective when confronted with dynamic objects in the real world. To enhance the robustness and localization accuracy of SLAM systems in dynamic scenes, this paper introduces a visual–inertial SLAM framework that integrates semantic and geometric information, called D-VINS. This paper begins by presenting a method for dynamic object classification based on the current motion state of features, enabling the identification of temporary static features within the environment. Subsequently, a feature dynamic check module is devised, which utilizes inertial measurement unit (IMU) prior information and geometric constraints from adjacent frames to calculate dynamic factors. This module also validates the classification outcomes of the temporary static features. Finally, a dynamic adaptive bundle adjustment module is developed, utilizing the dynamic factors of the features to adjust their weights during the nonlinear optimization process. The proposed methodology is evaluated using both public datasets and a dataset created specifically for this study. The experimental results demonstrate that D-VINS stands as one of the most real-time, accurate, and robust systems for dynamic scenes, showcasing its effectiveness in challenging real-world scenes.

Currently, the four major Global Navigation Satellite Systems (GNSS) are capable of supporting multi-frequency signals, and Real-time (RT) State Space Representation (SSR) correction products have been enriched and improved. Multi-GNSS, multi-frequency observations and multiple satellite RT SSR correction products present new opportunities to implement precise RT time transfer. This study extends the traditional post-processing dual-frequency undifferenced (UD) Precise Point Positioning (PPP) time transfer to RT multi-frequency scenarios, establishes models for dual-frequency, triple-frequency, quad-frequency and penta-frequency multi-GNSS PPP time transfer, and evaluates the product availability, orbit accuracy and clock offset accuracy of RT SSR products from BKG, CNE, DLR, ESA, GFZ, GMV, WHU, and IGS. A multi-frequency and multi-GNSS RT PPP time transfer experiment was conducted by selecting data from four stations, BRUX, PTBB, ONSA and SPT0, with precise atomic clocks mounted for one week. The accuracy of various SSR products in RT PPP time transfer was compared, and the performance of BDS/Galileo combination dual-frequency to penta-frequency RT UD PPP time transfer was evaluated. The experimental results show that the standard deviation and frequency stability difference of RT PPP time transfer among each SSR product is relatively small, especially after combining multi-systems. The frequency stability tends to be entirely consistent. In general, the WHU, GFZ, GMV, and WHU SSR products offer notable advantages in terms of accuracy and frequency stability for GPS, GLONASS, Galileo, and BDS RT PPP time transfer, respectively. Multi-GNSS and multi-frequency signals have both enhanced RT PPP time transfer performance, with a particularly noticeable improvement in terms of accuracy and short-term frequency stability. The effect of multi-GNSS on RT PPP time transfer is more pronounced than that of multi-frequency alone. Considering the higher costs associated with multi-frequency GNSS receivers, prioritizing multi-GNSS for RT PPP time transfer is recommended.