Navigation Requirements Research Articles

Optimization of Inbound and Outbound Vessel Scheduling in One-Way Channel Based on Reinforcement Learning

As the size and number of ships continue to grow, effective management of vessel scheduling has become more and more important for the efficient one-way channel port operation, whose characteristics significantly affect the safety and efficiency of ports. This paper presents a reinforcement-learning-based approach to optimize the scheduling of vessels in a one-way channel, aiming to quickly identify a scheduling solution that enhances port operational efficiency. This method models the vessel scheduling problem in a one-way channel by incorporating navigational constraints, safety requirements, and vessel-specific characteristics. Using the Q-learning algorithm to minimize vessel wait times, it identifies an optimal scheduling solution. Experiments were conducted using real data from the Dayao Bay Pier of Dalian Port to validate the rationality and effectiveness of the proposed model and algorithm. The results show that the reinforcement learning approach achieved approximately a 16% improvement in solution quality compared to the genetic algorithm (GA) while requiring only half the computation time. Additionally, it reduced delay times by over 40% relative to the traditional FCFS strategy, indicating superior overall performance. This research presents an efficient, intelligent approach to vessel scheduling, providing a theoretical foundation for further advancements in this field and enhancing decision support for vessel scheduling in one-way channels with practical implications.

An Integrated Algorithm Fusing UWB Ranging Positioning and Visual–Inertial Information for Unmanned Vehicles

During the execution of autonomous tasks within sheltered space environments, unmanned vehicles demand highly precise and seamless continuous positioning capabilities. While the existing visual–inertial-based positioning methods can provide accurate poses over short distances, they are prone to error accumulation. Conversely, radio-based positioning techniques could offer absolute position information, yet they encountered difficulties in sheltered space scenarios. Usually, three or more base stations were required for localization. To address these issues, a binocular vision/inertia/ultra-wideband (UWB) combined positioning method based on factor graph optimization was proposed. This approach incorporated UWB ranging and positioning information into the visual–inertia system. Based on a sliding window, the joint nonlinear optimization of multi-source data, including IMU measurements, visual features, as well as UWB ranging and positioning information, was accomplished. Relying on visual inertial odometry, this methodology enabled autonomous positioning without the prerequisite for prior scene knowledge. When UWB base stations were available in the environment, their distance measurements or positioning information could be employed to institute global pose constraints in combination with visual–inertial odometry data. Through the joint optimization of UWB distance or positioning measurements and visual–inertial odometry data, the proposed method precisely ascertained the vehicle’s position and effectively mitigated accumulated errors. The experimental results indicated that the positioning error of the proposed method was reduced by 51.4% compared to the traditional method, thereby fulfilling the requirements for the precise autonomous navigation of unmanned vehicles in sheltered space.

Head pose-assisted localization of facial landmarks for enhanced fast registration in skull base surgery.

In skull base surgery, the method of using a probe to draw or 3D scanners to acquire intraoperative facial point clouds for spatial registration presents several issues. Manual manipulation results in inefficiency and poor consistency. Traditional registration algorithms based on point clouds are highly dependent on the initial pose. The complexity of registration algorithms can also extend the required time. To address these issues, we used an RGB-D camera to capture real-time facial point clouds during surgery. The initial registration of the 3D model reconstructed from preoperative CT/MR images and the point cloud collected during surgery is accomplished through corresponding facial landmarks. The facial point clouds collected intraoperatively often contain rotations caused by the free-angle camera. Benefit from the close spatial geometric relationship between head pose and facial landmarks coordinates, we propose a facial landmarks localization network assisted by estimating head pose. The shared representation head pose estimation module boosts network performance by enhancing its perception of global facial features. The proposed network facilitates the localization of landmark points in both preoperative and intraoperative point clouds, enabling rapid automatic registration. A free-view human facial landmarks dataset called 3D-FVL was synthesized from clinical CT images for training. The proposed network achieves leading localization accuracy and robustness on two public datasets and the 3D-FVL. In clinical experiments, using the Artec Eva scanner, the trained network achieved a concurrent reduction in average registration time to 0.28 s, with an average registration error of 2.33 mm. The proposed method significantly reduced registration time, while meeting clinical accuracy requirements for surgical navigation. Our research will help to improving the efficiency and quality of skull base surgery.

A Lightweight Approach to Understand Forest Roads for New Nnergy Vehicles

Review A Lightweight Approach to Understand Forest Roads for New Nnergy Vehicles Luping Wang 1,*, Yuan Feng 1, Shanshan Wang 2, and Hui Wei 3 1 Laboratory of 3D Scene Understanding and Visual Navigation, School of Mechanical Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai 200093, China 2 Intel Asia-Pacific Research & Development Ltd., No.880 Zixing Road, Shanghai 201100, China 3 Laboratory of Algorithms for Cognitive Models, School of Computer Science, Fudan University, No. 825 Zhangheng Road, Shanghai 201203, China * Correspondence: 15110240007@fudan.edu.cn Received: 16 June 2024; Revised: 21 October 2024; Accepted: 24 October 2024; Published: 11 November 2024 Abstract: Scene understanding is a core issue for autonomous vehicles. However, its implementation has been thwarted by various outstanding issues, such as understanding forest roads in unknown field environments. Traditional three-dimensional (3D) point clouds or 3D estimation of fused data consume large amounts of memory and energy, making these methods less reliable in new energy vehicles with limited computational, memory, and energy resources. In this study, we propose a lightweight method to understand forest roads using a low-cost monocular camera. We extracted and clustered spatially similar texture projections based on oblique effect. Through the relative relationship between vanishing points and texture projections, contour lines can be estimated. After that, searching for the corresponding supporting evidence lines, we can segment the surface of the forest road, which can provide a decision basis for the automatic driving control system of new energy vehicles with limited resources. Unlike deep learning methods that are extremely resource-consuming, the proposed method requires no prior training, no calibration, and no internal parameters of the camera. At the same time, pure geometric reasoning makes the method robust to the ever-changing colors and lighting in the forest environment. The percentage of correctly classified pixels is compared to the ground truth. The experimental results show that the method can successfully understand forest roads and meet the requirements of autonomous navigation in forest environments for new energy vehicles with limited resources.

Comparative Analysis of Next-Generation Space Computing Applications on AMD-Xilinx Versal Architecture

Space computing has unique considerations that limit the achievable performance capabilities of onboard processing. Due to these limiting factors, current state-of-the-art devices for space computing are unable to meet the performance and energy-efficiency requirements for next-generation communication, navigation, and artificial-intelligence applications planned for future science and exploration missions. Space designers are transitioning to domain-specific architectures with specialized acceleration hardware, such as the AMD-Xilinx Versal Adaptive System-on-Chip, to address these issues. This heterogeneous platform provides developers with several processing subsystems that allow for mission-specific customization, including scalar processing units, programmable logic, and AI engine technology enabled by vector processing units. In this paper, performance, power-consumption, energy-efficiency, and resource-utilization tradeoffs for each Versal processing subsystem are evaluated by benchmarking a suite of representative next-generation artificial-intelligence and communication applications for space. This evaluation yields a design tradespace that mission designers can use to determine the Pareto-optimal solution for an artificial-intelligence or communication application based on performance requirements and power constraints. Additionally, this evaluation demonstrates that the performance and energy-efficiency benefits of the AI engines for space computing are significant; however, they are curtailed by considerably more power consumption than the scalar processors and programmable logic.

Hierarchization of sustainability indicators applied to inland waterway transport: a study in the Brazilian Amazon

The Brazilian Amazon, a vital ecosystem for global sustainability, has significant potential for utilizing inland waterway transport (IWT) as a catalyst for socioeconomic advancement. However, the exploitation of waterways has frequently led to adverse impacts on riverside ecosystems, underscoring the urgent need to identify IWTs that balance navigation and cargo distribution requirements while safeguarding sustainable development objectives. For this reason, the utilization of sustainability indicators in IWT has emerged as pivotal for comprehensively assessing its environmental, social, and economic dimensions. To address this need, our study proposes a hierarchical framework of sustainability indicators tailored to cargo transport along the IWT of the Brazilian Amazon. Initially, 256 indicators were identified, 26 of which were selected for hierarchical analysis. Subsequently, a qualitative survey involving 30 specialists representing diverse backgrounds and expertise was conducted employing the analytic hierarchy process methodology. The findings highlighted the vital importance of environmental indicators. This result is aligned with the 2030 Agenda. Prioritizing investments following this hierarchy seems to have the power to catalyze socioeconomic development within the Brazilian Amazon (and possibly in other IWTs).

Adapting a Telehealth Physical Activity and Diet Intervention to a Co-Designed Website for Self-Management After Stroke: Tutorial.

People who experience a stroke are at a higher risk of recurrent stroke when compared with people who have not had a stroke. Addressing modifiable risk factors like physical inactivity and poor diet has been shown to improve blood pressure, a leading contributor to stroke. However, survivors of stroke often experience challenges with accessing risk reduction services including long wait lists, difficulty with transportation, fatigue, impaired function, and diminished exercise capacity. Providing health interventions via a website can extend the reach when compared with programs that are only offered face to face or via real-time telehealth. Given global challenges of accessing secondary prevention programs, it is important to consider alternative ways that this information can be made available to survivors of stroke worldwide. Using the "design thinking" framework and drawing on principles of the integrated knowledge translation approach, we adapted 2 co-designed telehealth programs called i-REBOUND - Let's get moving (physical activity intervention) and i-REBOUND - Eat for health (diet Intervention) to create the i-REBOUND after stroke website. The aim of this paper is to describe the systematic process undertaken to adapt resources from the telehealth delivered i-REBOUND - Let's get moving and i-REBOUND - Eat for health programs to a website prototype with a focus on navigation requirements and accessibility for survivors of stroke. We engaged a variety of key stakeholders with diverse skills and expertise in areas of stroke recovery, research, and digital health. We established a governance structure, formed a consumer advisory group, appointed a diverse project team, and agreed on scope of the project. Our process of adaptation had the following 3 phases: (1) understand, (2) explore, (3) materialize. Our approach considered the survivor of stroke at the center of all decisions, which helped establish guiding principles related to our prototype design. Careful and iterative engagement with survivors of stroke together with the application of design thinking principles allowed us to establish the functional requirements for our website prototype. Through user testing, we were able to confirm the technical requirements needed to build an accessible and easy-to-navigate website catering to the unique needs of survivors of stroke. We describe the process of adapting existing content and co-creating new digital content in partnership with, and featuring, people who have lived experience of stroke. In this paper, we provide a road map for the steps taken to adapt resources from 2 telehealth-delivered programs to a website format that meets specific navigation and accessibility needs of survivors of stroke.

Research on multi-source information fusion based on sliding window-factor graph for autonomous underwater vehicle

Aiming at the high-precision real-time navigation requirements of autonomous underwater vehicle (AUV)-integrated navigation system, an AUV multi-source information fusion algorithm based on sliding window-factor graph (SW-FG) was proposed in this paper. First, the SW-FG model for the AUV-integrated navigation system is constructed using the factor graph (FG) theory. Second, historical factor nodes and estimated variable nodes are selected by a fixed-width window, and the information factors are updated by window sliding and updating. Finally, the forward and backward message passing of the FG are performed within the sliding window to achieve the smoothed optimal estimation of the current navigation state through the weighted combination of the two navigation solutions. Simulation results show that the proposed SW-FG algorithm can significantly enhance the real-time navigation solution accuracy of the AUV-integrated navigation system. Compared with the regular FG filtering algorithm, the horizontal positioning accuracy of the proposed SW-FG algorithm is improved by 17.17%. Semi-physical experimental results verify the reliability and effectiveness of the proposed scheme, and the horizontal positioning accuracy is improved by 7.28%.

Research on the Positioning and Tracking Technology of Surgical Instruments Based on Laser Localization

Abstract Addressing the complexities and low accuracy of traditional surgical instrument positioning processes, a low-cost, high-precision surgical instrument tracking and positioning strategy is proposed. By integrating the VR laser positioning system, Lighthouse, with oral medicine, the positioning process and principles of the system are elucidated. Based on the initial system calibration, static positioning accuracy tests were conducted, and the positioning and tracking of the surgical instrument endpoints were achieved through the least squares method. Experimental results demonstrate that both static and dynamic positioning errors of the system are less than 0.3mm, meeting the requirements of oral surgical navigation.

An Adaptive and Automatic Power Supply Distribution System with Active Landmarks for Autonomous Mobile Robots.

With the development of automation and intelligent technologies, the demand for autonomous mobile robots in the industry has surged to alleviate labor-intensive tasks and mitigate labor shortages. However, conventional industrial mobile robots' route-tracking algorithms typically rely on passive markers, leading to issues such as inflexibility in changing routes and high deployment costs. To address these challenges, this study proposes a novel approach utilizing active landmarks-battery-powered luminous landmarks that enable robots to recognize and adapt to flexible navigation requirements. However, the reliance on batteries necessitates frequent recharging, prompting the development of an automatic power supply system. This system integrates omnidirectional contact electrodes on mobile robots, allowing to recharge active landmarks without precise positional alignment. Despite these advancements, challenges such as the large size of electrodes and non-adaptive battery charging across landmarks persist, affecting system efficiency. To mitigate these issues, this research focuses on miniaturizing active landmarks and optimizing power distribution among landmarks. The experimental results of this study demonstrated the effectiveness of our automatic power supply method and the high accuracy of landmark detection. Our power distribution calculation method can adaptively manage energy distribution, improving the system's persistence by nearly three times. This study aims to enhance the practicality and efficiency of mobile robot remote control systems utilizing active landmarks by simplifying installation processes and extending operational durations with adaptive and automatic power supply distribution.

A study on path-planning algorithm for a multi-section continuum robot in confined multi-obstacle environments

Abstract In confined multi-obstacle environments, generating feasible paths for continuum robots is challenging due to the need to avoid obstacles while considering the kinematic limitations of the robot. This paper deals with the path-planning algorithm for continuum robots in confined multi-obstacle environments to prevent their over-deformation. By modifying the tree expansion process of the Rapidly-exploring Random Tree Star (RRT*) algorithm, a path-planning algorithm called the continuum-RRT* algorithm herein is proposed to achieve fewer iterations and faster convergence as well as generating desired paths that adhere to the kinematic limitations of the continuum robots. Then path planning and path tracking are implemented on a tendon-driven four-section continuum robot to validate the effectiveness of the path-planning algorithm. The path-planning results show that the path generated by the algorithm indeed has fewer transitions, and the path generated by the algorithm is closer to the optimal path that satisfies the kinematic limitations of the continuum robot. Furthermore, path-tracking experiments validate the successful navigation of the continuum robot along the algorithm-generated path, exhibiting an error range of 2.51%–3.91%. This attests to the effectiveness of the proposed algorithm in meeting the navigation requirements of continuum robots.

Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on improved multi-objective particle swarm optimization algorithm

This paper presents an improved multi-objective particle swarm optimization (IMOPSO) algorithm for cooperative path planning of multiple autonomous underwater vehicles (multi-AUVs) in 3D underwater terrain and vortex environment. Traditional PSO algorithms often struggle with local optima in high-dimensional, complex search spaces. To address this, we propose enhancements including Sine double-sequence perturbation interpolation for initial particle population generation, adaptive differential evolution strategies, iterative local search, and traction operations to enhance global search capability and avoid local optima. Additionally, cubic spline interpolation is employed to smooth the planned paths. The objective function is tailored to the ocean currents environment, considering path length, smoothness, deflection angle, and current impact, with a penalty function method to handle multi-AUVs cooperation and environmental constraints optimization for optimal energy consumption. Simulation results demonstrate that the proposed algorithm effectively plans short-range, high-security global paths for multi-AUVs, verifying its effectiveness and practicality in meeting the navigation requirements in ocean currents environments.

SceneFusion: Room-Scale Environmental Fusion for Efficient Traveling Between Separate Virtual Environments.

Traveling between scenes has become a major requirement for navigation in numerous virtual reality (VR) social platforms and game applications, allowing users to efficiently explore multiple virtual environments (VEs). To facilitate scene transition, prevalent techniques such as instant teleportation and virtual portals have been extensively adopted. However, these techniques exhibit limitations when there is a need for frequent travel between separate VEs, particularly within indoor environments, resulting in low efficiency. In this article, we first analyze the design rationale for a novel navigation method supporting efficient travel between virtual indoor scenes. Based on the analysis, we introduce the SceneFusion technique that fuses separate virtual rooms into an integrated environment. SceneFusion enables users to perceive rich visual information from both rooms simultaneously, achieving high visual continuity and spatial awareness. While existing teleportation techniques passively transport users, SceneFusion allows users to actively access the fused environment using short-range locomotion techniques. User experiments confirmed that SceneFusion outperforms instant teleportation and virtual portal techniques in terms of efficiency, workload, and preference for both single-user exploration and multi-user collaboration tasks in separate VEs. Thus, SceneFusion presents an effective solution for seamless traveling between virtual indoor scenes.

Nonuniformity of diffractive splitting and receiver-transmitter alignment for large-field-of-view hundred-beam-scale LiDAR

To meet the spatial perception requirements for autonomous ship navigation in common scenarios such as ocean navigation, port entry and exit, and lock passage, commercial vehicle-mounted LiDAR technology falls short of demands in aspects such as sensing distance, field of view, angular resolution, and spatial sampling density. This paper proposes a hundred-beam-scale LiDAR scheme based on large-field-of-view diffractive beam splitting and a fiber array for echo reception and presents an in-depth investigation of the angular nonuniformity of diffractive beam splitting and the microradian-scale alignment for such hundred-beam-scale large-field-of-view LiDAR. This paper considers a combination of split-beam transmission based on a high-order diffractive optical element (DOE) and echo reception based on a high-precision fiber-optic line array. The nonlinear angular characteristics of the DOE are deduced and analyzed for a large field of view. The units of the receiving fiber-optic array are designed to offset the influence of the angular nonlinearity of the DOE, ensuring high-precision receiver–transmitter alignment of the hundred-beam-scale LiDAR for beams at any order of diffraction and helping to reduce system errors. The above-described LiDAR system has undergone laboratory testing and practical engineering verification, and it provides a new optical solution for LiDAR systems at the hundred-beam scale with a large field of view, a small divergence angle, and high sampling density. The presented system achieves a registration accuracy of 66.5 μrad with 128 beams and a 10-degree field of view, greatly improving signal reception efficiency. Such LiDAR systems have a wide range of applications, including space docking, target identification, lunar and planetary exploration, and ground-based vehicle-mounted LiDAR.

A curved path extraction method using RGB-D multimodal data for single-edge guided navigation in irregularly shaped fields

Due to the undulating topography, the world contains numerous irregularly shaped small fields. Autonomous navigation along the curved edges of these fields is crucial for achieving complete unmanned operation in these small-field environments. Based on this, we proposed an accurate and robust curved path extraction method using RGB-D multimodal data for single-edge guided navigation in irregularly shaped fields. Leveraging the significant height difference between headland and farmland, we employed an efficient RGB-D multimodal semantic segmentation network to achieve high-precision segmentation of headland area. Meanwhile, a slope-based feature points adaptive selection algorithm and a feature points transformation algorithm fusing multimodal depth data were proposed innovatively for curved navigation path extraction. By utilizing the RGB-D segmentation model ESANet, we achieved a mean intersection over union (mIoU) of 96.94 %, surpassing other RGB segmentation models. The mean deviations between the ground truth path and the extracted path were less than 6.43 pixels on an image with a resolution of 640 × 480. The proposed method can finally achieve a processing efficiency of 23.80 FPS on images in 640 × 480 resolution, and the field experiments tested that the mean deviations of the agricultural vehicle navigating along the extracted curved navigation path were less than 0.188 m, which met the real-time performance requirement and accuracy requirement of autonomous navigation.

Research on four-sided differential localization algorithm for automated guided vehicles based on ultra-wideband

Abstract To meet the high-precision positioning and navigation requirements of automated guided vehicles (AGV) in environments such as factories and workshops, this study utilizes ultra-wideband (UWB) positioning technology and designs a multi-tag differential localization algorithm to enhance the positioning accuracy of AGV. Starting from the current research status of AGV positioning and navigation, the paper provides a detailed introduction to the concept, process, and practical applications of the UWB differential localization algorithm. The maximum static positioning accuracy for UWB is 64 mm, and the maximum dynamic positioning accuracy is 222 mm, thereby meeting the requirements for AGV positioning and navigation.

A Visual-Inertial Pressure Fusion-Based Underwater Simultaneous Localization and Mapping System.

Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%.

Improved A* Navigation Path-Planning Algorithm Based on Hexagonal Grid

Navigation systems are extensively used in everyday life, but the conventional A* algorithm has several limitations in path planning applications within these systems, such as low degrees of freedom in path planning, inadequate consideration of the effects of special regions, and excessive nodes and turns. Addressing these limitations, an enhanced A* algorithm was proposed using regular hexagonal grid mapping. First, the approach to map modeling using hexagonal grids was described. Subsequently, the A* algorithm was refined by optimizing the calculation of movement costs, thus allowing the algorithm to integrate environmental data more effectively and flexibly adjust node costs while ensuring path optimality. A quantitative method was also introduced to assess map complexity and adaptive heuristics that decrease the number of traversed nodes and increase the search speed. Moreover, a turning penalty measure was implemented to minimize unnecessary turns on the planned paths. Simulation results confirmed that the improved A* algorithm exhibits superior performance, which can dynamically adjust movement costs, enhance search efficiency, reduce turns, improve overall path planning quality, and solve critical path planning issues in navigation systems, greatly aiding the development and design of these systems and making them better suited to meet modern navigation requirements.

Local Path Planner for Mobile Robot Considering Future Positions of Obstacles

Local path planning is a necessary ability for mobile robot navigation, but existing planners are not sufficiently effective at dynamic obstacle avoidance. In this article, an improved timed elastic band (TEB) planner based on the requirements of mobile robot navigation in dynamic environments is proposed. The dynamic obstacle velocities and TEB poses are fully integrated through two-dimensional (2D) lidar and multi-obstacle tracking. First, background point filtering and clustering are performed on the lidar points to obtain obstacle clusters. Then, we calculate the data association matrix of the obstacle clusters of the current and previous frame so that the clusters can be matched. Thirdly, a Kalman filter is adopted to track clusters and obtain the optimal estimates of their velocities. Finally, the TEB poses and obstacle velocities are associated: we predict the obstacle position corresponding to the TEB pose through the detected obstacle velocity and add this constraint to the corresponding TEB pose vertex. Then, a pose sequence considering the future positions of obstacles is obtained through a graph optimization algorithm. Compared with the original TEB, our method reduces the total running time by 22.87%, reduces the running distance by 19.23%, and increases the success rate by 21.05%. Simulations and experiments indicate that the improved TEB enables robots to efficiently avoid dynamic obstacles and reach the goal as quickly as possible.

RFG-TVIU: robust factor graph for tightly coupled vision/IMU/UWB integration.

High precision navigation and positioning technology, as a fundamental function, is gradually occupying an indispensable position in the various fields. However, a single sensor cannot meet the navigation requirements in different scenarios. This paper proposes a "plug and play" Vision/IMU/UWB multi-sensor tightly-coupled system based on factor graph. The difference from traditional UWB-based tightly-coupled models is that the Vision/IMU/UWB tightly-coupled model in this study uses UWB base station coordinates as parameters for real-time estimation without pre-calibrating UWB base stations. Aiming at the dynamic change of sensor availability in multi-sensor integrated navigation system and the serious problem of traditional factor graph in the weight distribution of observation information, this study proposes an adaptive robust factor graph model. Based on redundant measurement information, we propose a novel adaptive estimation model for UWB ranging covariance, which does not rely on prior information of the system and can adaptively estimate real-time covariance changes of UWB ranging. The algorithm proposed in this study was extensively tested in real-world scenarios, and the results show that the proposed system is superior to the most advanced combination method in all cases. Compared with the visual-inertial odometer based on the factor graph (FG-VIO), the RMSE is improved by 62.83 and 64.26% in scene 1 and 82.15, 70.32, and 75.29% in scene 2 (non-line-of-sight environment).