Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
Global Navigation Satellite Systems (GNSS) have undergone more than half a century of development and construction, with more than a hundred navigation satellites currently providing precise and reliable positioning, navigation, and timing (PNT) services for various users. Meanwhile, efficient utilization of these satellites has become a topic of interest. Selecting an appropriate satellite set in a proper manner can reduce computational burden while ensuring positioning accuracy. Geometric Dilution of Precision (GDOP) is commonly used in satellite selection as it quantifies the impact of satellite geometry on positioning accuracy. Due to its computational simplicity, GDOP has been widely applied in satellite selection, but it only considers the satellite geometric configuration while ignoring the quality of satellite observations. As a result, the selected satellite set may lead to poor positioning accuracy. To address this issue, we use a satellite selection criterion based on the combination of near-real-time accuracy of satellite observations and geometric configuration. This criterion utilizes the combination of Geometry-Free Ionosphere-Free (GFIF) and Melbourne–Wübbena (MW) linear combinations of observations. Through a sliding window, we estimate the near-real-time accuracy of observations and use it to calculate the Weighted Geometric Dilution of Precision (WGDOP) for satellite selection. In a global International GNSS Service (IGS) station validation experiment, the satellite set selected based on WGDOP using near-real-time accuracy of GFIF and MW observations improved overall positioning accuracy by 11.6% and 12% when compared with the GDOP-based selection, and by 6% and 6.4% when compared with the Signal-to-Noise Ratio (SNR) weighting method. In a low-cost device validation experiment, the satellite selection method based on near-real-time accuracy of GFIF and MW improved positioning accuracy by 22.5% and 19.7% when compared with the GDOP-based method, and by 23.3% and 20.5% when compared with the SNR-based method. A set of dynamic observation experiments further demonstrates that the satellite selection method based on the near-real-time accuracy of GFIF and MW combinations outperforms the other two selection criteria in dynamic scenarios....
The vehicle kinematics model aided inertial navigation system (VKM-AINS) is an effective solution for autonomous navigation in unmanned ground vehicles. However, traditional VKM-AINS methods suffer from fast error divergence and unstable VKM accuracy, leading to degraded performance in situations of long-duration global navigation satellite system failure, challenging road conditions, and high-dynamics motion. To address these challenges, this paper proposes a novel VKM-AINS incorporating the state transformation (ST) error model and variational Bayesian (VB)-based adaptive estimation algorithm. First, the error coupling problem is analyzed, which is one of the root causes for the fast error divergence, and the state transition function and observation function are developed with the ST error model to decouple the effects of the velocity and attitude error. Second, the issue of the time-varying observation noise covariance matrix, arising from the unstable VKM performance across different motion states, is analyzed. A VB-based filter is proposed to simultaneously approximate the distributions of observation noise and state variables, enabling optimal estimation despite the varying driving states. Field experiments validate the performance improvement of the proposed method, demonstrating a 19.07% reduction in horizontal position error compared to conventional VKM-AINS, while maintaining real-time computational efficiency....
Coronectomy is a conservative surgical technique used to manage deeply impacted mandibular third molars at high risk of inferior alveolar nerve injury. Precise execution is essential to avoid complications, particularly in cases with limited surgical access. Dynamic navigation (DN) systems may enhance accuracy and safety in such procedures. This report describes the 5-year follow-up of a DN-assisted coronectomy in a 42-year-old patient presenting with recurrent pericoronitis and a pericoronal lesion associated with a deeply impacted lower third molar. Preoperative planning was performed using cone beam computed tomography (CBCT), and DN was employed intraoperatively to guide surgical instrumentation in real time. The procedure was carried out according to a standardized protocol, including crown sectioning, root reduction, and primary closure. No intraoperative or early postoperative complications were observed. At 5-year follow-up, the patient was asymptomatic. Clinical examination showed complete mucosal healing and normal probing depths. Radiographic evaluation revealed retained roots without signs of pathology and bone formation distal to the second molar. This case may highlight the potential role of DN in improving surgical control during coronectomy in anatomically complex situations, contributing to a favorable long-term clinical and radiographic outcome....
Deploying autonomous vehicles in urban mobility systems promises significant improvements in safety, efficiency, and sustainability. On the other hand, running these vehicles in the continuously changing and often uncertain conditions of modern cities turns out to be a major challenge. These cars need advanced systems that can continuously change in order to observe conditions. This paper puts forward a new way that brings together multiple LIDAR sensors for the real-time spotting and following of objects, along with adaptive motion planning methods made to handle the difficulties of city traffic. Using LIDAR-based mapping for environmental modeling and predictive tracking techniques helps the system build a richly detailed, consistently updating depiction of surroundings that supports accurate and quick decisions. Another feature of the system is dynamic path planning that ensures safe navigation by considering traffic, pedestrian movement, and road conditions. Simulations carried out in highly dense urban scenarios show improvement in collision avoidance, path-planning optimization, and response to environmental dynamics. Such outcomes prove that combining multi-LIDAR tracking and adaptive motion planning contributes significantly to the performance and safety of an autonomous vehicle when operating in very complex urban conditions....
We present a framework for visual teach-and-repeat (VTR) navigation designed to operate robustly in environments characterized by variable or low light levels. First, we show that navigation accuracy for VTR can be improved by integrating a topological map with a decision-making strategy designed to reduce latencies and trajectory error. Specifically, a local scene descriptor, acquired through deep learning, is coupled with stereo camera imaging and a proportional-integral controller to compensate for inaccuracies in visual matching. This approach facilitates accurate teach-and-repeat navigation with correction for odometry drift with respect to both orientation and along-route error accumulation using only monocular images during route following. Next, we adapt this general approach to operate with an off-the-shelf event-based camera and an event-based local descriptor model. Experiments in a night-time urban environment demonstrate that this event-based system provides improved and robust navigation accuracy in low-light environments when compared with a conventional camera paired with a state-of-the-art RGB-based descriptor model. Overall, high trajectory accuracy is demonstrated for VTR navigation in both indoor and outdoor environments using deep-learned descriptors, whilst the extension to event-based vision extends the capability of VTR navigation to a wider range of challenging environments....
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