Explore the vast range of titles available.

With the growing deployment of autonomous driving and unmanned systems in road environments, efficiently and accurately performing environmental perception and map construction has become a significant challenge for SLAM systems. In this paper, we propose an innovative SLAM framework comprising a frontend tracking network called SGF-net and a backend filtering mechanism, namely Semantic Gaussian Filter. This framework effectively suppresses dynamic objects by integrating feature point detection and semantic segmentation networks, filtering out Gaussian point clouds that degrade mapping quality, thus enhancing system performance in complex outdoor scenarios. The inference speed of SGF-net has been improved by over 23% compared to non-fused networks. Specifically, we introduce SGF-SLAM (Semantic Gaussian Filter SLAM), a dynamic mapping framework that shields dynamic objects undergoing temporal changes through multi-view geometry and semantic segmentation, ensuring both accuracy and stability in mapping results. Compared with existing methods, our approach can efficiently eliminate pedestrians and vehicles on the street, restoring an unobstructed road environment. Furthermore, we present a map update function, which is aimed at updating areas occluded by dynamic objects by using semantic information. Experiments demonstrate that the proposed method significantly enhances the reliability and adaptability of SLAM systems in road environments.

With the growing demand for autonomous robotic operations in complex and unstructured environments, traditional semantic SLAM systems—which rely on closed-set semantic vocabularies—are increasingly limited in their ability to robustly perceive and understand diverse and dynamic scenes. This paper focuses on the paradigm shift toward open-world semantic scene understanding in SLAM and provides a comprehensive review of the technological evolution from closed-world assumptions to open-world frameworks. We survey the current state of research in open-world semantic SLAM, highlighting key challenges and frontiers. In particular, we conduct an in-depth analysis of three critical areas: zero-shot open-vocabulary understanding, dynamic semantic expansion, and multimodal semantic fusion. These capabilities are examined for their crucial roles in unknown class identification, incremental semantic updates, and multisensor perceptual integration. Our main contribution is presenting the first systematic algorithmic benchmarking and performance comparison of representative open-world semantic SLAM systems, revealing the potential of these core techniques to enhance semantic understanding in complex environments. Finally, we propose several promising directions for future research, including lightweight model deployment, real-time performance optimization, and collaborative multimodal perception, and offering a systematic reference and methodological guidance for continued advancements in this emerging field.

The integration of SLAM with Gaussian splatting presents a significant challenge: achieving compatibility between real-time performance and high-quality rendering. This paper introduces a novel SLAM system named SFGS-SLAM (SuperFeats Gaussian Splatting SLAM), restructured from tracking to mapping, to address this issue. A new keypoint detection network is designed and characterized by fewer parameters than existing networks such as SuperFeats, resulting in faster processing speeds. This keypoint detection network is augmented with a global factor graph incorporating the GICP (Generalized Iterative Closest Point) odometry, reprojection-error factors and loop-closure constraints to minimize drift. It is integrated with the Gaussian splatting as the mapping part. By leveraging the reprojection error, the proposed system further reduces odometry error and improves rendering quality without compromising speed. It is worth noting that SFGS-SLAM is primarily designed for static indoor environments and does not explicitly model or suppress dynamic disturbances. Comprehensive experiments were conducted on various datasets to evaluate the performance of our system. Extensive experiments on indoor and synthetic datasets show that SFGS-SLAM achieves accuracy comparable to state-of-the-art SLAM while running in real time. SuperFeats reduces matching latency by over 50%, and joint optimization significantly improves global consistency. Our results demonstrate the practicality of combining lightweight feature matching with dense Gaussian mapping, highlighting trade-offs between speed and accuracy.

This research presents an intelligent beam-hopping-based grant-free random access (GFRA) architecture designed for secure Internet of Things (IoT) communications in Low Earth Orbit (LEO) satellite networks. In light of the difficulties associated with facilitating extensive device connectivity while ensuring low latency and high reliability, we present a beam-hopping GFRA (BH-GFRA) scheme that enhances access efficiency and reduces resource collisions. Three distinct resource-hopping schemes, random hopping, group hopping, and orthogonal group hopping, are examined and utilized within the framework. This technique utilizes orthogonal resource allocation algorithms to facilitate efficient resource sharing, effectively tackling the irregular and dynamic traffic. Also, a kind of activity mechanism is proposed based on the constraints of the spatio-temporal distribution of devices. We assess the system’s performance through a thorough mathematical analysis. Furthermore, we ascertain the access delay and success rate to evaluate its capability to serve a substantial number of IoT devices under satellite–terrestrial delay and interference of massive connections. The suggested method demonstrably improves connection, stability, and access efficiency in 6G IoT satellite networks, meeting the rigorous demands of next-generation IoT applications.

To enhance positioning capability and reliability within existing Communication Navigation Fusion Systems (CNFSs), this paper proposes an Asymmetric Double-Sideband Composite Localization Signal (ADCLS) and a dual-carrier aggregation dual-code loop tracking mechanism with fuzzy control. By organically integrating an embedded signal into the original positioning signal, the code loop is optimized via fuzzy control, while the ADCLS signal is processed as an asymmetric double-sideband signal for joint signal extraction. Experimental validation employs the 5G New Radio (NR) Time-Delay Line (TDL) channel model to simulate multipath propagation effects. The results show that this method improves the tracking accuracy of the code loop and the main carrier loop, thereby enhancing the ranging accuracy.

In indoor environments where Global Navigation Satellite System (GNSS) signals are entirely blocked, wireless signals such as 5G and Ultra-Wideband (UWB) have become primary means for high-precision positioning. However, complex indoor structures lead to significant multipath effects, which severely constrain the improvement of positioning accuracy. Existing indoor positioning methods rarely link environmental semantic information (e.g., wall, column) to multipath error estimation, leading to inaccurate multipath correction—especially in complex scenes with multiple reflective objects. To address this issue, this paper proposes a LiDAR-assisted multipath estimation and positioning method. This method constructs a tightly coupled perception-positioning framework: first, a semantic-feature-based neural network for reflective surface detection is designed to accurately extract the geometric parameters of potential reflectors from LiDAR point clouds; subsequently, a unified factor graph model is established to multidimensionally associate and jointly infer terminal states, virtual anchor (VA) states, wireless signal measurements, and LiDAR-perceived reflector information, enabling dynamic discrimination and utilization of both line-of-sight (LOS) and non-line-of-sight (NLOS) paths. Experimental results demonstrate that the root mean square error (RMSE) of the proposed method is improved by 32.1% compared to traditional multipath compensation approaches. This research provides an effective solution for high-precision and robust positioning in complex indoor environments.

The sixth generation (6G) satellite twin network is an important solution to achieve seamless global coverage of 6G. The deterministic geometric topology and the randomness of the communication behaviors of 6G networks limit the realism and transparency of cross-platform and cross-object communication, twin, and computing co-simulation networks. Meanwhile, the parallel-based serverless architecture has a high redundancy of computational resource allocation. Therefore, for the first time, we present a new hypergraph hierarchical nested kriging model, which provides theoretical analysis and modeling of integrated relationships for communication, twin, and computing. We explore the hierarchical unified characterization method which joins heterogeneous topologies. A basis function matrix for local flexible connectivity of the global network is designed for the connection of huge heterogeneous systems to decouple the resource mapping among heterogeneous networks. To improve the efficiency of resource allocation in communication, twin, and computing integrated network, a multi-constraint multi-objective genetic algorithm (MMGA) based on the common requirements of operations, storage, interaction, and multi-layer optimal solution conflict is proposed for the first time. The effectiveness of the algorithm and architecture is verified through simulation and testing.

Positioning service is a critical technology that bridges the physical world with digital information, significantly enhancing efficiency and convenience in life and work. The evolution of 5G technology has proven that positioning services are integral components of current and future cellular networks. However, positioning accuracy is hindered by non-line-of-sight (NLoS) propagation, which severely affects the measurements of angles and delays. In this study, we introduced a deep autoencoding channel transform-generative adversarial network model that utilizes line-of-sight (LoS) samples as a singular category training set to fully extract the latent features of LoS, ultimately employing a discriminator as an NLoS identifier. We validated the proposed model in 5G indoor and indoor factory (dense clutter, low base station) scenarios by assessing its generalization capability across different scenarios. The results indicate that, compared to the state-of-the-art method, the proposed model markedly diminished the utilization of device resources and achieved a 2.15% higher area under the curve while reducing computing time by 12.6%. This approach holds promise for deployment in future positioning terminals to achieve superior localization precision, catering to commercial and industrial Internet of Things applications.

Unmanned aerial vehicles (UAVs) are increasingly used indoors for inspection, security, and emergency tasks. Achieving accurate and robust localization under Global Navigation Satellite System (GNSS) unavailability and obstacle occlusions is therefore a critical challenge. Due to their inherent physical limitations, Inertial Measurement Unit (IMU)–based localization errors accumulate over time, Ultra-Wideband (UWB) measurements suffer from systematic biases in Non-Line-of-Sight (NLOS) environments and Visual–Inertial Odometry (VIO) depends heavily on environmental features, making it susceptible to long-term drift. We propose a tightly coupled fusion framework based on the Error-State Kalman Filter (ESKF). Using an IMU motion model for prediction, the method incorporates raw UWB ranges, VIO relative poses, and TFmini altitude in the update step. To suppress abnormal UWB measurements, a multi-epoch outlier rejection method constrained by VIO is developed, which can robustly eliminate NLOS range measurements and effectively mitigate the influence of outliers on observation updates. This framework improves both observation quality and fusion stability. We validate the proposed method on a real-world platform in an underground parking garage. Experimental results demonstrate that, in complex indoor environments, the proposed approach exhibits significant advantages over existing algorithms, achieving higher localization accuracy and robustness while effectively suppressing UWB NLOS errors as well as IMU and VIO drift.

In complex environments, the application of traditional Kalman Filtering in GNSS/INS integrated navigation systems often encounters challenges such as filter divergence and accuracy degradation. This paper introduces the technique of Simplified Spherical Unscented Kalman Filtering (SSUKF) and, based on this, proposes an Adaptive Simplified Spherical Unscented Kalman Filtering (ASSUKF) integrated navigation method. This approach, built upon SSUKF, incorporates an adaptive filter that effectively utilizes residuals and innovation sequences to mitigate the divergence phenomenon during the filtering process. Furthermore, the system is capable of online estimation and dynamic adjustment of the statistical characteristics of measurement noise, leading to more accurate state estimation and significantly enhancing the adaptive capability of SSUKF. ASSUKF improves position accuracy in the latitude direction by 18.10% and in the longitude direction by 20.6%. For attitude error, ASSUKF performs exceptionally well. Specifically, the pitch angle error improves by 27.6% compared to UKF and by 27.1% compared to SSUKF. The roll angle error improves by 29.9% compared to UKF and by 20.1% compared to SSUKF. The heading angle error improves by 24.3% compared to SSUKF, validating the method’s substantial advantages in improving system accuracy and robustness, demonstrating its effectiveness and potential in complex environments.