Precise Navigation Research Articles

Enhancing unmanned ground vehicle performance in SAR operations: integrated gesture-control and deep learning framework for optimised victim detection.

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camera-based victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm-Stacked Convolutional Neural Network-Bi-Long Short Term Memory-Gated Recurrent Unit (MOA-SConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purpose-built robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster.

Hilbert signal envelope-based multi-features methods for GNSS spoofing detection

The Global Navigation Satellite System (GNSS) provides precise Positioning, Navigation, and Timing (PNT) service for various industries such as military and national economy. However, civil GNSS is fragile and vulnerable to spoofing for the weak signal strength and the open architecture. To address the problem of limited performance of existing detection methods due to the strong concealment of spoofing, this paper proposes a novel scheme using Hilbert Signal Envelope-based multi-features. Firstly, multi-features are picked and calculated from the receiving signal, especially we propose a Tweaked Signal Quality Monitoring (TSQM) metric in this step. Secondly, an algorithm for extracting Hilbert Signal Envelope (HSE) is proposed for enhancing the features, which reduces features redundancy and pushes clean and spoofing samples away from each other. Thirdly, the enhanced features are sent to train and test the Machine Learning (ML) models. Finally, experimental validation is conducted on the Texas Spoofing Test Battery (TEXBAT) dataset and measurement data collected by low-cost Software-Defined Radio (SDR). The Area Under Curve (AUC) of the proposed method improves by 5.34 % and 3.07 % (reaching 97.34 % and 98.44 %) compared to the latest multi-parameters method on TEXBAT and measurement data, respectively, which provides an effective scheme for spoofing detection.

Interconnection and damping assignment passivity‐based control for dynamic steering position stabilization of an underactuated AUV

AbstractSteering motion bestows autonomous underwater vehicles (AUVs) with the agility to navigate intricate paths and trajectories precisely. Ensuring effective steering position stabilization in underwater vehicles is paramount, as it enables precise navigation and enhances safety, efficiency, data accuracy, adaptability to changing conditions, and the overall success of diverse underwater missions. This article addresses the challenging task of steering position stabilization in underactuated AUVs. To achieve this, we employ an interconnection and damping assignment passivity‐based control method to design a control law tailored for steering position stabilization. Our approach considers the nonlinear dynamics of a six‐degrees‐of‐freedom steering motion in AUVs. The control objective involves assigning a suitable energy function and reshaping the interconnection and damping structure to render the closed‐loop system asymptotically stable at the desired equilibrium point. The robustness of our proposed control law is assessed rigorously, subjecting it to modeling uncertainties and underwater disturbances. Our findings are substantiated with simulation results that support the efficacy of the designed control law. Notably, we base our simulations on experimentally validated steering motion parameters obtained from the REMUS 100 AUV, enhancing the real‐world applicability of our research.

BIPOOLNET: An advanced UNet architecture for enhanced lane detection in autonomous vehicles

In the rapidly evolving landscape of autonomous vehicle technology, the imperative to bolster safety within smart urban ecosystems has never been more critical. This endeavor requires the deployment of advanced detection systems capable of navigating the intricacies of pedestrian, near-vehicle, and lane detection challenges, with a particular focus on the nuanced requirements of curved lane navigation – a domain where traditional AI models exhibit notable deficiencies. This paper introduces BIPOOLNET, an innovative encoder-decoder neural architecture, ingeniously augmented with a feature pyramid to facilitate the precise delineation of curved lane geometries. BIPOOLNET integrates max pooling and average pooling to extract critical features and mitigate the complexity of the feature map, redefining the benchmarks for lane detection technology. Rigorous evaluation using the TuSimple dataset underscores BIPOOLNET’s exemplary performance, evidenced by an unprecedented accuracy rate of 98.45%, an F1-score of 98.17%, and notably minimal false positive (1.84%) and false negative (1.09%) rates. These findings not only affirm BIPOOLNET’s supremacy over extant models but also signal a paradigm shift in enhancing the safety and navigational precision of autonomous vehicles, offering a scalable, robust solution to the multifaceted challenges posed by real-world driving dynamics.

Improved YOLO v7 for Sustainable Agriculture Significantly Improves Precision Rate for Chinese Cabbage (Brassica pekinensis Rupr.) Seedling Belt (CCSB) Detection

Precise navigation in agricultural applications necessitates accurate guidance from the seedling belt, which the Global Positioning System (GPS) alone cannot provide. The overlapping leaves of Chinese cabbage (Brassica pekinensis Rupr.) present significant challenges for seedling belt fitting due to difficulties in plant identification. This study aims to address these challenges by improving the You Only Look Once (YOLO) v7 model with a novel approach that decouples its network head deriving from the Faster-Regions with Convolutional Neural Network (Faster R-CNN) architecture. Additionally, this study introduced a BiFormer attention mechanism to accurately identify the centers of overlapping Chinese cabbages. Using these identified centers and pixel distance verification, this study achieved precise fitting of the Chinese cabbage seedling belt (CCSB). Our experimental results demonstrated a significant improvement in performance metrics, with our improved model achieving a 2.5% increase in mean average precision compared to the original YOLO v7. Furthermore, our approach attained a 94.2% accuracy in CCSB fitting and a 91.3% Chinese cabbage identification rate. Compared to traditional methods such as the Hough transform and linear regression, our method showed an 18.6% increase in the CCSB identification rate and a 17.6% improvement in angle accuracy. The novelty of this study lies in the innovative combination of the YOLO v7 model with a decoupled head and the BiFormer attention mechanism, which together advance the identification and fitting of overlapping leafy vegetables. This advancement supports intelligent weeding, reduces the reliance on chemical herbicides, and promotes safer, more sustainable agricultural practices. Our research not only improves the accuracy of overlapping vegetable identification, but also provides a robust framework for enhancing precision agriculture.

Template-Guided Silicon Micromotor Assembly for Enhanced Cell Manipulation.

Light-driven micro/nanorobots (LMNRs) are tiny, untethered machines with great potential in fields like precision medicine, nano manufacturing, and various other domains. However, their practicality hinges on developing light-manipulation strategies that combine versatile functionalities, flexible design options, and precise controllability. Our study introduces an innovative approach to construct micro/nanorobots (MNRs) by utilizing micro/nanomotors as fundamental building blocks. Inspired by silicon Metal-Insulator-Semiconductor (MIS) solar cell principles, we design a new type of optomagnetic hybrid micromotors (OHMs). These OHMs have been skillfully optimized with integrated magnetic constituent, resulting in efficient light propulsion, precise magnetic navigation, and the potential for controlled assembly. One of the key features of the OHMs is their ability to exhibit diverse motion modes influenced by fracture surfaces and interactions with the environment, streamlining cargo conveyance along "micro expressway"-the predesigned microchannels. Further enhancing their versatility, a template-guided assembly strategy facilitates the assembly of these micromotors into functional microrobots, encompassing various configurations such as "V-shaped", "N-shaped", and 3D structured microrobots. The heightened capabilities of these microrobots, underscore the innovative potential inherent in hybrid micromotor design and assembly, which provides a foundational platform for the realization of multi-component microrobots. Our work moves a step toward forthcoming microrobotic entities boasting advanced functionalities.

Template‐Guided Silicon Micromotor Assembly for Enhanced Cell Manipulation

AbstractLight‐driven micro/nanorobots (LMNRs) are tiny, untethered machines with great potential in fields like precision medicine, nano manufacturing, and various other domains. However, their practicality hinges on developing light‐manipulation strategies that combine versatile functionalities, flexible design options, and precise controllability. Our study introduces an innovative approach to construct micro/nanorobots (MNRs) by utilizing micro/nanomotors as fundamental building blocks. Inspired by silicon Metal‐Insulator‐Semiconductor (MIS) solar cell principles, we design a new type of optomagnetic hybrid micromotors (OHMs). These OHMs have been skillfully optimized with integrated magnetic constituent, resulting in efficient light propulsion, precise magnetic navigation, and the potential for controlled assembly. One of the key features of the OHMs is their ability to exhibit diverse motion modes influenced by fracture surfaces and interactions with the environment, streamlining cargo conveyance along “micro expressway”—the predesigned microchannels. Further enhancing their versatility, a template‐guided assembly strategy facilitates the assembly of these micromotors into functional microrobots, encompassing various configurations such as “V‐shaped”, “N‐shaped”, and 3D structured microrobots. The heightened capabilities of these microrobots, underscore the innovative potential inherent in hybrid micromotor design and assembly, which provides a foundational platform for the realization of multi‐component microrobots. Our work moves a step toward forthcoming microrobotic entities boasting advanced functionalities.

Integrating Cosmic Microwave Background Readings with Celestial Navigation to Enhance Deep Space Navigation.

This research unveils a cutting-edge navigation system for deep space missions that utilizes cosmic microwave background (CMB) sensor readings to enhance spacecraft positioning and velocity estimation accuracy significantly. By exploiting the Doppler-shifted CMB spectrum and integrating it with optical measurements for celestial navigation, this approach employs advanced data processing through the Unscented Kalman Filter (UKF), enabling precise navigation amid the complexities of space travel. The simulation results confirm the system's exceptional precision and resilience in deep space missions, marking a significant advancement in astronautics and paving the way for future space exploration endeavors.

MAZE SLOVING ROBOT WITH LIVE MONITORING

This project presents a sophisticated maze-solving robot, equipped with advanced live monitoring capabilities, meticulously designed to autonomously navigate and solve intricate maze environments.The robot's navigation prowess is underpinned by an array of sensors, prominently featuring ultrasonic and infrared technologies, which are adept at detecting and avoiding obstacles while simultaneously mapping the layout of the maze. These sensors feed data into an embedded microcontroller, which processes the information in real-time. To determine the optimal path to the maze's exit, the microcontroller employs sophisticated algorithms such as Depth-First Search (DFS) or Breadth-First Search (BFS), renowned for their efficiency in solving complex mazes.A key feature of the robot is its live monitoring capability, enabled by a wireless communication module. This module is responsible for transmitting critical data, including the robot's status, position, and sensory inputs, to a remote monitoring station. The real-time feedback provided through this system allows users to observe the robot's progress dynamically and make necessary adjustments on the fly, ensuring optimal performance and adaptability in diverse environments. The robot’s compact chassis design ensures superior maneuverability, even in the most confined spaces, while a robust power management system guarantees extended operational periods, making the robot both efficient and reliable.The integration of live monitoring capabilities not only significantly enhances the robot's functionality but also offers invaluable insights into its performance and decision-making processes. This feature is particularly advantageous for educational purposes, providing students and researchers with a tangible demonstration of theoretical concepts in robotics and automation. Additionally, the real-time data acquisition and analysis capabilities make the robot a potent tool for research, especially in fields requiring precise navigation and obstacle avoidance, such as search and rescue operations. Here, the ability to receive immediate feedback and monitor the robot’s actions in real-time is crucial, as it can directly influence the success of missions in dynamic and potentially hazardous environments.The development of this maze-solving robot represents a significant leap forward in the realms of robotics, automation, and remote monitoring technologies. By addressing real-world challenges with enhanced efficiency, this project underscores the potential of advanced robotics to transform a variety of applications. The robot's design, characterized by a harmonious blend of sophisticated sensor arrays, intelligent pathfinding algorithms, and robust real-time monitoring systems, exemplifies the cutting-edge advancements in technology. This convergence of features not only demonstrates the practical applications of theoretical research but also paves the way for future innovations in autonomous systems, highlighting their capability to operate effectively in real-world scenarios and to provide critical insights that can drive further technological progress. Keywords: maze-solving robot, autonomous navigation, live monitoring, ultrasonic sensors, infrared sensors, flood-fill algorithm, real-time processing, wireless communication.

Best Integer Equivariant Estimation Based on Unsupervised Machine Learning for GNSS Precise Positioning and Navigation in Complex Environments

Best Integer Equivariant Estimation Based on Unsupervised Machine Learning for GNSS Precise Positioning and Navigation in Complex Environments

Towards Precision Navigation: Curved lane Detection System in Vehicles

Abstract: Curved lane detection is a critical componentof advanced driver assistance systems (ADAS) and autonomous vehicles, playing a pivotal role in ensuring safeand efficient navigation on complex roadways. In the pursuitof enhancing accuracy and expediting processing speed in road lane detection, researchers have proposed numerous methodologies. Despite these efforts, several challenges persist, including variations in lane markings, fluctuations in lighting conditions, and the emergence of shadows. Addressing these obstacles is crucial for establishing dependable lane detection systems. The curve lane detection algorithm exhibits a robust performance, as evidenced by the experimental results, which consistently reveal a notably high success rate in accurately identifying and delineating curved lanes

Robotic Guardians: Navigating Hospital Isolation Zones for Disease Control

Abstract: In healthcare contexts where human access is limited, particularly in isolation and quarantine rooms, autonomous solutions become critical. This study presents a novel solution to addressing this problem: a line-following robot built for use in hospital isolation units. The robot's operating principle is led by a color recognition system, which responds dynamically to certain colors linked with directing cues. The system's ability to capture vital information by utilizing the ESP32 camera module increases its utility beyond navigation, extending to the collection of patient vitals displayed beside beds. By combining precise navigation with information gathering capabilities, this line-following robot offers a potential solution for tasks in restricted access healthcare environments.

Engineered Microrobots for Targeted Delivery of Bacterial Outer Membrane Vesicles (OMV) in Thrombus Therapy.

In the realm of thrombosis treatment, bioengineered outer membrane vesicles (OMVs) offer a novel and promising approach, as they have rich content of bacterial-derived components. This study centers on OMVs derived from Escherichia coli BL21 cells, innovatively engineered to encapsulate the staphylokinase-hirudin fusion protein (SFH). SFH synergizes the properties of staphylokinase (SAK) and hirudin (HV) to enhance thrombolytic efficiency while reducing the risks associated with re-embolization and bleeding. Building on this foundation, this study introduces two cutting-edge microrobotic platforms: SFH-OMV@H for venous thromboembolism (VTE) treatment, and SFH-OMV@MΦ, designed specifically for cerebral venous sinus thrombosis (CVST) therapy. These platforms have demonstrated significant efficacy in dissolving thrombi, with SFH-OMV@H showcasing precise vascular navigation and SFH-OMV@MΦ effectively targeting cerebral thrombi. The study shows that the integration of these bioengineered OMVs and microrobotic systems marks a significant advancement in thrombosis treatment, underlining their potential to revolutionize personalized medical approaches to complex health conditions.

Research on Improved Road Visual Navigation Recognition Method Based on DeepLabV3+ in Pitaya Orchard

Traditional DeepLabV3+ image semantic segmentation methods face challenges in pitaya orchard environments characterized by multiple interference factors, complex image backgrounds, high computational complexity, and extensive memory consumption. This paper introduces an improved visual navigation path recognition method for pitaya orchards. Initially, DeepLabV3+ utilizes a lightweight MobileNetV2 as its primary feature extraction backbone, which is augmented with a Pyramid Split Attention (PSA) module placed after the Atrous Spatial Pyramid Pooling (ASPP) module. This improvement enhances the spatial feature representation of feature maps, thereby sharpening the segmentation boundaries. Additionally, an Efficient Channel Attention Network (ECANet) mechanism is integrated with the lower-level features of MobileNetV2 to reduce computational complexity and refine the clarity of target boundaries. The paper also designs a navigation path extraction algorithm, which fits the road mask regions segmented by the model to achieve precise navigation path recognition. Experimental findings show that the enhanced DeepLabV3+ model achieved a Mean Intersection over Union (MIoU) and average pixel accuracy of 95.79% and 97.81%, respectively. These figures represent increases of 0.59 and 0.41 percentage points when contrasted with the original model. Furthermore, the model’s memory consumption is reduced by 85.64%, 84.70%, and 85.06% when contrasted with the Pyramid Scene Parsing Network (PSPNet), U-Net, and Fully Convolutional Network (FCN) models, respectively. This reduction makes the proposed model more efficient while maintaining high segmentation accuracy, thus supporting enhanced operational efficiency in practical applications. The test results for navigation path recognition accuracy reveal that the angle error between the navigation centerline extracted using the least squares method and the manually fitted centerline is less than 5°. Additionally, the average deviation between the road centerlines extracted under three different lighting conditions and the actual road centerline is only 2.66 pixels, with an average image recognition time of 0.10 s. This performance suggests that the study can provide an effective reference for visual navigation in smart agriculture.

IOT based Autonomous Location Surveillance System Using PNT Services

The entire set of interconnected devices as well as the technology enabling communication between the devices and the cloud. This article presents an innovative Internet of Things (IOT)based autonomous location monitoring system that is made possible by Positioning, Navigation, and Timing (PNT) services. The system allows for real-time tracking and surveillance of objects or persons by combining Internet of Things sensors and communication modules with PNT technologies like GPS, Galileo, or GLONASS. The technology uses advanced machine learning techniques and algorithms to automatically identify and evaluate anomalies in the monitored area. It then swiftly reports these findings to the relevant authorities.PNT services are seamlessly linked to ensure accurate and reliable location, even in challenging Environment. In addition to being adaptable, scalable, and compatible with existing infrastructure, the Technology has a wide range of applications, including border security, asset protection, and perimeter monitoring. This work advances the possibilities of autonomous surveillance systems. In depth features provided by the research include mobile number identification (subject to legal and regulatory constraints), real-time location visualization, and centralized vehicle data management for enhanced fleet management, security, and operational effectiveness The development of sophisticated solutions that make use of precision navigation and timing (PNT) and the internet of things (IOT) has been spurred by the growing need for effective location based surveillance systems. Edge computing units, a central control unit and a monitoring and control user interface are important parts of the system. Data is processed locally with edge computing, which lowers latency and improves response times. to create a self-contained location monitoring system using precise PNT services and IOT technology. Keywords: IOT, Real-Time Tracking, Vehicle Management, Python Flask, Mobile Number Tracking, Data Fetching

Forecasting 24‐Hr Total Electron Content With Long Short‐Term Memory Neural Network

AbstractAn accurate prediction of the ionospheric state is important for correcting ionospheric propagation effects on Global Navigation Satellite Systems (GNSS) signals used in precise navigation and positioning applications. The main objective of the present work is to find a total electron content (TEC) model which gives a good estimate of ionospheric state not only during quiet but also during perturbed ionospheric conditions. For this, we implemented several long short‐term memory (LSTM)‐based models capable of predicting TEC up to 24 hr ahead. For the first time, we used the solar wind forcing parameters Wprot (a measure of the ionospheric disturbance during storm time) and Econv (measure of the solar wind parameters) as driver parameters. We found that using external drivers does not improve the accuracy of TEC predictions significantly. The final model is trained with data from the last two solar cycles using TEC from the rapid UQRG global ionosphere maps (GIMs). Data from the years 2015 and 2020 were excluded from the training data set and used for testing. The performance of the LSTM‐based TEC model is tested for near real‐time (RT) cases as well by using RT products (IRTG GIMs) as historical TEC inputs. We compared the performance of the LSTM‐based model to a quiet‐time feed forward neural network (FNN)‐based model and the Neustrelitz TEC model (NTCM). The results indicate that the LSTM‐based model proposed here is outperforming the FNN‐based model and NTCM in both cases, that is, using the UQRG or the IRTG GIMs as input for the historical TEC.

Ex vivo validation of magnetically actuated intravascular untethered robots in a clinical setting

Intravascular surgical instruments require precise navigation within narrow vessels, necessitating maximum flexibility, minimal diameter, and high degrees of freedom. Existing tools often lack control during insertion due to undesirable bending, limiting vessel accessibility and risking tissue damage. Next-generation instruments aim to develop hemocompatible untethered devices controlled by external magnetic forces. Achieving this goal remains complex due to testing and implementation challenges in clinical environments. Here we assess the operational effectiveness of hemocompatible untethered magnetic robots using an ex vivo porcine aorta model. The results demonstrate a linear decrease in the swimming speed of untethered magnetic robots as arterial blood flow increases, with the capability to navigate against a maximum arterial flow rate of 67 mL/min. The untethered magnetic robots effectively demonstrate locomotion in a difficult-to-access target site, navigating through the abdominal aorta and reaching the distal end of the renal artery.

Feasibility and safety report on robotic assistance for cervical pedicle screw fixation: a cadaveric study

This cadaveric study aimed to evaluate the safety and usability of a novel robotic system for posterior cervical pedicle screw fixation. Three human cadaveric specimens and C2-T3 were included. Freshly frozen human cadaver specimens were prepared and subjected to robot-assisted posterior cervical pedicle screw fixation using the robotic system. The accuracy of screw placement, breach rate, and critical structure violations were evaluated. The results were statistically compared with those of previous studies that used different robotic systems for cervical pedicle screw fixation. The robotic system demonstrated a high accuracy rate in screw placement. A significant number of screws were placed within predetermined safe zones. The total entry offset was 1.08 ± 0.83 mm, the target offset was 1.86 ± 0.50 mm, and the angle offset was 2.14 ± 0.77°. Accuracy rates comparable with those of previous studies using different robotic systems were achieved. The system was also feasible, allowing precise navigation and real-time feedback during the procedure. This cadaveric study validated the safety and usability of the novel robotic system for posterior cervical pedicle screw fixation. The system exhibited high precision in screw placement, and the results support the extension of the indications for robot-assisted pedicle screw fixation from the lumbar spine to the cervical spine.

Navigation Techniques and Algorithms for Mobile Robots and Automated Guided Vehicles

Mobile robots are self-contained devices that can navigate and move around their surroundings. They observe their environment and create maps using different sensors such as lidar, Global Positioning System (GPS), and cameras. Automated Guided Vehicles (AGVs) are vehicles that can move independently utilizing different approaches such as underground cables, laser scanners, or GPS systems. This research investigates several navigation methodologies and algorithms used in AGVs and mobile robots. It focusses on the advantages of integrating lidar technology with other sensors as well as how it is used in local navigation techniques. We look at two motion planning techniques for obstacle avoidance: the Artificial Potential Field (APF) approach and the Vector Field Histogram (VFH) algorithm. Two effective techniques for finding the best routes are discussed: the A* algorithm and Dijkstra's algorithm. The various AGV types and their navigation systems (such as wired, guide, laser, vision-based, and gyro-based) are also examined in the study. Neural networks and fuzzy logic are investigated as AGV control techniques, with line following and obstacle avoidance as examples of their use. The study emphasizes how crucial precise and dependable navigation systems are to the effective and secure operation of mobile robots and AGVs.

A novel LiDAR-GNSS-INS Two-Phase Tightly Coupled integration scheme for precise navigation

Abstract. Recent advances in precise navigation have extensively utilized the integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), particularly in the domain of intelligent vehicles. However, the efficacy of such navigation systems is considerably compromised by the reflection and multipath disruptions of non-light-of-sight (NLOS) signals. Light Detection and Ranging (LiDAR)-based odometry, an active perception-based sensor known for its precise 3D measurements, has become increasingly prevalent in augmenting navigation systems. Nonetheless, the assimilation of LiDAR odometry with GNSS/INS systems presents substantial challenges. Addressing these challenges, this study introduces a two-phase sensor fusion (TPSF) approach that synergistically combines GNSS positioning, LiDAR odometry, and IMU pre-integration through a dual-stage sensor fusion process. The initial stage employs an Extended Kalman Filter (EKF) to amalgamate the GNSS solution with IMU Mechanization, facilitating the estimation of IMU biases and system initialization. Subsequently, the second stage integrates scan-to-map LiDAR odometry with IMU mechanization to support continuous LiDAR factor estimation. Factor graph optimization (FGO) is then utilized for the comprehensive fusion of LiDAR factors, IMU pre-integration, and GNSS solutions. The efficacy of the proposed methodology is corroborated through rigorous testing on a demanding trajectory from an urbanized open-source dataset, with the system demonstrating a notable enhancement in performance compared to the state-of-the-art algorithms, achieving a translational Standard Deviation (STD) of 1.269 meters.