Navigation System Research Articles

Drivers of Electric Vehicle Adoption in Nigeria

Electric vehicles (EVs) represent a significant advancement in automotive technology, utilizing electricity as a power source in place of traditional fossil fuels and incorporating sophisticated navigation and autopilot systems. These vehicles align with multiple Sustainable Development Goals (SDGs) by offering a more environmentally sustainable alternative to internal combustion engine vehicles (ICEVs). Despite their potential, the adoption of EVs in developing nations such as Nigeria remains constrained. The Unified Theory of Acceptance and Use of Technology (UTAUT) framework is expanded in this study by including important enablers such as poor infrastructure, problems with affordability, and government support in the broader category of facilitating conditions. Additionally, it scrutinizes variables such as trust, performance expectations, social influences, and network externalities to identify the primary determinants influencing Nigerian consumers' propensity to adopt EVs. Results show that the percentage increase of H6 (facilitating conditions → behavioral intentions) compared to H5 (network externalities → behavioral intentions) is approximately 32.35%, indicating that traditional drivers significantly influence individuals' willingness to purchase EVs and are particularly strong factors in adoption decisions. The paper concludes with a discussion of these findings and proposes strategies for future research to further explore the barriers and drivers of EV adoption in Nigeria.

Spacecraft design for interstellar travel

This paper endeavours to introduce and elucidate potential mechanisms aimed at the development of a spacecraft that is not only sustainable but also optimized for interstellar travel to the Andromeda galaxy, with the primary objective of scouting for potentially habitable exoplanets suitable for human colonization. The conceptual framework encompasses a comprehensive analysis of various critical components essential for the functionality and longevity of the spacecraft, including but not limited to propulsion systems, attitude control mechanisms, and advanced navigation systems. In addition, habitable areas which are also called Goldilocks zones refer to the areas around stars where planetary conditions are conducive to foster lives. As the existence of liquid water is known as the fundamental prerequisite for supporting life, the basic criteria for a habitable planet is temperature appropriate for water sustaining. The final analysis shows that space immigration is a determined consequence of human being and people should take effective measures to investigate future possible ways of immigrating to another planet.

Current Advancements in Drone Technology for Medical Sample Transportation

Background: The integration of drone technology into healthcare logistics presents a significant opportunity to enhance the speed, reliability, and efficiency of medical sample transportation. Methods: This paper provides a narrative review of current advancements in drone technology, focusing on its application in the rapid and secure delivery of medical samples, particularly in urban and remote regions where traditional transportation methods often face challenges. Drawing from recent studies and case reports, the review highlights the role of technologies such as artificial intelligence (AI)-driven navigation systems, real-time monitoring, and secure payload management in mitigating logistical barriers like traffic congestion and geographical isolation. Results: Based on findings from various case studies, the review demonstrates how drones can significantly reduce transportation time and costs, while improving accessibility to healthcare services in underserved areas. Conclusions: This paper concludes that, while challenges such as regulatory hurdles and privacy concerns remain, ongoing technological advancements and the development of supportive regulatory frameworks have the potential to revolutionize medical logistics, ultimately improving patient outcomes and healthcare delivery.

Prediction of Ionospheric Scintillations Using Machine Learning Techniques during Solar Cycle 24 across the Equatorial Anomaly

Ionospheric scintillation is a pressing issue in space weather studies due to its diverse effects on positioning, navigation, and timing (PNT) systems. Developing an accurate and timely prediction model for this event is crucial. In this work, we developed two machine learning models for the prediction of ionospheric scintillation events at the equatorial anomaly during the maximum and minimum phases of solar cycle 24. The models developed in this study are the Random Forest (RF) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm. The models take inputs based on the solar wind parameters obtained from the OMNI Web database from the years 2010–2017 and Pc5 wave power obtained from the Bear Island (BJN) magnetometer station. We retrieved data from the Scintillation Network and Decision Aid (SCINDA) receiver in Egypt from which the S4 index was computed to quantify amplitude scintillations that were utilized as the target in the model development. Out-of-sample model testing was performed to evaluate the prediction accuracy of the models on unseen data after training. The similarity between the observed and predicted scintillation events, quantified by the R2 score, was 0.66 and 0.74 for the RF and XGBoost models, respectively. The corresponding Root Mean Square Errors (RMSEs) associated with the models were 0.01 and 0.01 for the RF and XGBoost models, respectively. The similarity in error shows that the XGBoost model is a good and preferred choice for the prediction of ionospheric scintillation events at the equatorial anomaly. With these results, we recommend the use of ensemble learning techniques for the study of the ionospheric scintillation phenomenon.

Should voice navigation prompts be earlier and more complicated? A study of drivers’ visual attention, physiology and operating performance

Voice navigation systems can help drivers access information about road conditions and make correct decisions, but inappropriate information level and information prompt timing may lead to untimely processing of information, distraction, or even misjudgment of drivers, which in turn may cause safety hazards. The purpose of this study is to investigate the effects of voice information level and information prompt timing on drivers’ driving behavior and physiological characteristics. Twenty-seven participants were recruited to conduct driving simulator experiments. The results showed that the driver’s visual attention is more focused and the driving performance is better when the voice navigation prompts contain four information units (driving directions, traffic facility information, distance information, and road condition information at the same time). Moreover, when the voice navigation prompts with four information units were played 300 meters from the intersection, the driver’s overall driving performance was the highest. The results offer additional theoretical insights for the design of voice navigation systems, aiding drivers in accurately understanding driving conditions and providing them with ample reaction time to enhance their driving decisions.

Computed tomography and structured light imaging guided orthopedic navigation puncture system: effective reduction of intraoperative image drift and mismatch.

Image-guided surgical navigation systems are widely regarded as the benchmark for computer-assisted surgical robotic platforms, yet a persistent challenge remains in addressing intraoperative image drift and mismatch. It can significantly impact the accuracy and precision of surgical procedures. Therefore, further research and development are necessary to mitigate this issue and enhance the overall performance of these advanced surgical platforms. The primary objective is to improve the precision of image guided puncture navigation systems by developing a computed tomography (CT) and structured light imaging (SLI) based navigation system. Furthermore, we also aim to quantifying and visualize intraoperative image drift and mismatch in real time and provide feedback to surgeons, ensuring that surgical procedures are executed with accuracy and reliability. A CT-SLI guided orthopedic navigation puncture system was developed. Polymer bandages are employed to pressurize, plasticize, immobilize and toughen the surface of a specimen for surgical operations. Preoperative CT images of the specimen are acquired, a 3D navigation map is reconstructed and a puncture path planned accordingly. During surgery, an SLI module captures and reconstructs the 3D surfaces of both the specimen and a guiding tube for the puncture needle. The SLI reconstructed 3D surface of the specimen is matched to the CT navigation map via two-step point cloud registrations, while the SLI reconstructed 3D surface of the guiding tube is fitted by a cylindrical model, which is in turn aligned with the planned puncture path. The proposed system has been tested and evaluated using 20 formalin-soaked lower limb cadaver specimens preserved at a local hospital. The proposed method achieved image registration RMS errors of 0.576 ± 0.146 mm and 0.407 ± 0.234 mm between preoperative CT and intraoperative SLI surface models and between preoperative and postoperative CT surface models. In addition, preoperative and postoperative specimen surface and skeletal drifts were 0.033 ± 0.272 mm and 0.235 ± 0.197 mm respectively. The results indicate that the proposed method is effective in reducing intraoperative image drift and mismatch. The system also visualizes intraoperative image drift and mismatch, and provides real time visual feedback to surgeons.

Study and Comparison of Hyperbolic and Pseudorange Positioning Algorithms in the eLoran System

The positioning algorithms of the Enhanced Long-Range Navigation (eLoran) system primarily include the hyperbolic positioning algorithm and the pseudorange positioning algorithm. However, the calculations present in the existing literature are inaccurate and lack empirical data, and a thorough and precise comparison of the two algorithms has yet to be conducted. Therefore, this paper employs a combination of simulation analysis and empirical analysis to explore these two positioning algorithms in depth, with an optimization of the initial position calculation in the pseudorange algorithm. Under ideal conditions without observational errors, through precise calculations and analysis, the positioning errors of both algorithms are approximately zero, and full-area solutions can be achieved. Under conditions with observational errors, this study shows that both algorithms exhibit positioning errors, with the pseudorange algorithm achieving a level of accuracy comparable to that of the hyperbolic algorithm. At the same time, the empirical analysis further verified this conclusion. Additionally, this study found that the pseudorange positioning algorithm demonstrated better applicability in practical applications, as it successfully resolved the multivalued and singularity issues present in the hyperbolic positioning algorithm.

YOLO-RWY: A Novel Runway Detection Model for Vision-Based Autonomous Landing of Fixed-Wing Unmanned Aerial Vehicles

In scenarios where global navigation satellite systems (GNSSs) and radio navigation systems are denied, vision-based autonomous landing (VAL) for fixed-wing unmanned aerial vehicles (UAVs) becomes essential. Accurate and real-time runway detection in VAL is vital for providing precise positional and orientational guidance. However, existing research faces significant challenges, including insufficient accuracy, inadequate real-time performance, poor robustness, and high susceptibility to disturbances. To address these challenges, this paper introduces a novel single-stage, anchor-free, and decoupled vision-based runway detection framework, referred to as YOLO-RWY. First, an enhanced data augmentation (EDA) module is incorporated to perform various augmentations, enriching image diversity, and introducing perturbations that improve generalization and safety. Second, a large separable kernel attention (LSKA) module is integrated into the backbone structure to provide a lightweight attention mechanism with a broad receptive field, enhancing feature representation. Third, the neck structure is reorganized as a bidirectional feature pyramid network (BiFPN) module with skip connections and attention allocation, enabling efficient multi-scale and across-stage feature fusion. Finally, the regression loss and task-aligned learning (TAL) assigner are optimized using efficient intersection over union (EIoU) to improve localization evaluation, resulting in faster and more accurate convergence. Comprehensive experiments demonstrate that YOLO-RWY achieves AP50:95 scores of 0.760, 0.611, and 0.413 on synthetic, real nominal, and real edge test sets of the landing approach runway detection (LARD) dataset, respectively. Deployment experiments on an edge device show that YOLO-RWY achieves an inference speed of 154.4 FPS under FP32 quantization with an image size of 640. The results indicate that the proposed YOLO-RWY model possesses strong generalization and real-time capabilities, enabling accurate runway detection in complex and challenging visual environments, and providing support for the onboard VAL systems of fixed-wing UAVs.

In-patient neurosurgical tumor treatments for malignant glioma patients in Germany.

Treatment for malignant gliomas involves multiple disciplines, including neurosurgery, radiation therapy, medical and neuro-oncology, and palliative medicine, with function-preserving neurosurgical tumor removal being crucial. However, real-world data on hospital cases, treatment types, especially regarding surgical approaches, and the associated complication and mortality rates in Germany are lacking. We analyzed data on hospital cases involving malignant gliomas (ICD-10-GM code C71) from the German §21 Hospital Remuneration Act, provided by the Institute for the Hospital Remuneration System (InEK GmbH), from 2019 to 2022. Our focus was on neuro-oncological operations defined by the German Cancer Society (DKG) and included specific operation and procedure (OPS) codes. From 2019 to 2022, there were 101,192 hospital cases involving malignant gliomas in Germany. Neurosurgical tumor removal was performed in 27,193 cases (26.9%). Microsurgical techniques were used in 95% of surgeries, intraoperative navigation systems in 84%, fluorescence-guided surgeries in 45.6%, and intraoperative neurophysiological monitoring (IONM) in 46.4%. Surgical or medical complications occurred in 2903 cases (10.7%). The hospital mortality rate was 2.7%. Mortality was significantly higher in patients aged 65 and older (Odds ratio 2.9, p < 0.0001), and lower in cases using fluorescence-guided procedures (Odds ratio 0.8, p = 0.015) and IONM (Odds ratio 0.5, p < 0.0001). Over the course of 4years, over 100,000 hospital cases involving adult patients diagnosed with malignant gliomas were treated in Germany, with 27,193 cases undergoing tumor removal using various modern surgical techniques. The hospital mortality rate was 2.7%.

Технологии и методы планирования перемещения БПЛА по маршрутным точкам

Unmanned aerial vehicles (UAVs) occupy a special place in our world. Their ability to navigate along set routes opens up prospects in various fields. The purpose of the study is to review and analyze navigation systems and UAV routing algorithms, methods that allow UAVs to follow the route with high accuracy. GPS and inertial navigation (INS) systems that provide accurate location determination are being investigated. The capabilities of sensor systems — cameras, lidars and ultrasonic sensors — are analyzed to detect obstacles and adjust the trajectory; voxel maps for three-dimensional modeling of the environment and methods of simultaneous localization and mapping (SLAM); algorithm A* (A-star); genetic routing algorithm, potential-based obstacle avoidance algorithms and RRT. Practical significance: the use of these methods and technologies can significantly improve the safety and accuracy of UAV routing, the ability to navigate autonomously in complex and dynamically changing landscapes. In conclusion, the advantages and limitations of navigation approaches and technologies are discussed; the importance of integrating sensor systems and SLAM methods to increase the autonomy and efficiency of UAVs; directions for further research.

Advancing Autonomous Vehicle Navigation through Hybrid Fuzzy-Neural Network Training Systems

In the evolving realm of autonomous vehicle navigation, the integration of fuzzy logic and neural networks presents a formidable challenge, particularly in the context of real-time, on-the-fly neural network training. This paper addresses the gap in dynamic and adaptable training methods necessary for navigating unpredictable environments with limited computational resources. The primary objective of our study is to empirically validate a hybrid training approach that combines fuzzy logic with back-propagation learning algorithms, aiming to optimize neural network performance under hardware constraints. Our methodology leverages a fuzzy logic trainer to provide initial training sets dynamically, which guide the neural network in adjusting its weights in real time, thus facilitating adaptive learning during navigation tasks. The findings reveal that this integrated approach not only enhances the learning efficiency of neural networks but also significantly improves navigation accuracy in real-time scenarios. These advancements contribute to the field by demonstrating the feasibility of deploying more adaptable and robust autonomous navigation systems, potentially expanding their application in more diverse and challenging environments.

2D/3D registration based on biplanar X-ray and CT images for surgical navigation

Background and Objectives:Image-based 2D/3D registration is a crucial technology for fluoroscopy-guided surgical interventions. However, traditional registration methods relying on a single X-ray image into surgical navigation systems. This study proposes a novel 2D/3D registration approach utilizing biplanar X-ray images combined with computed tomography (CT) to significantly reduce registration and navigation errors. The method is successfully implemented in a surgical navigation system, enhancing its precision and reliability. Methods:First, we simultaneously register the frontal and lateral X-ray images with the CT image, enabling mutual complementation and more precise localization. Additionally, we introduce a novel similarity measure for image comparison, providing a more robust cost function for the optimization algorithm. Furthermore, a multi-resolution strategy is employed to enhance registration efficiency. Lastly, we propose a more accurate coordinate transformation method, based on projection and 3D reconstruction, to improve the precision of surgical navigation systems.Results: We conducted registration and navigation experiments using pelvic, spinal, and femur phantoms. The navigation results demonstrated that the feature registration errors (FREs) in the three experiments were 0.505±0.063 mm, 0.515±0.055 mm, and 0.577±0.056 mm, respectively. Compared to the point-to-point (PTP) registration method based on anatomical landmarks, our method reduced registration errors by 31.3%, 23.9%, and 26.3%, respectively. Conclusion:The results demonstrate that our method significantly reduces registration and navigation errors, highlighting its potential for application across various anatomical sites. Our code is available at: https://github.com/SJTUdemon/2D-3D-Registration

One-shot entorhinal maps enable flexible navigation in novel environments.

Animals must navigate changing environments to find food, shelter or mates. In mammals, grid cells in the medial entorhinal cortex construct a neural spatial map of the external environment1-5. However, how grid cell firing patterns rapidly adapt to novel or changing environmental features on a timescale relevant to behaviour remains unknown. Here, by recording over 15,000 grid cells in mice navigating virtual environments, we tracked the real-time state of the grid cell network. This allowed us to observe and predict how altering environmental features influenced grid cell firing patterns on a nearly instantaneous timescale. We found evidence that visual landmarks provide inputs to fixed points in the grid cell network. This resulted in stable grid cell firing patterns in novel and altered environments after a single exposure. Fixed visual landmark inputs also influenced the grid cell network such that altering landmarks induced distortions in grid cell firing patterns. Such distortions could be predicted by a computational model with a fixed landmark to grid cell network architecture. Finally, a medial entorhinal cortex-dependent task revealed that although grid cell firing patterns are distorted by landmark changes, behaviour can adapt via a downstream region implementing behavioural timescale synaptic plasticity6. Overall, our findings reveal how the navigational system of the brain constructs spatial maps that balance rapidity and accuracy. Fixed connections between landmarks and grid cells enable the brain to quickly generate stable spatial maps, essential for navigation in novel or changing environments. Conversely, plasticity in regions downstream from grid cells allows the spatial maps of the brain to more accurately mirror the external spatial environment. More generally, these findings raise the possibility of a broader neural principle: by allocating fixed and plastic connectivity across different networks, the brain can solve problems requiring both rapidity and representational accuracy.

Visual Navigation Systems for Maritime Smart Ships: A Survey

The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are first studied. Then, it analyzes the development of smart ships for navigation from various fields such as the technology industry and regulation. Then, the importance of navigation technology is analyzed, and the current status of key technologies of navigation systems is deeply analyzed. Meanwhile, this paper also focuses on single perception technology and integrated perception technology based on single perception technology. As the development of artificial intelligence means that intelligent shipping is inevitably the trend for future shipping, this paper analyzes the future development trend of smart ships and visual navigation systems, providing a clear perspective on the future direction of visual navigation technology for smart ships.

Development status and challenges of anti-spoofing technology of GNSS/INS integrated navigation

The threat of spoofing interference has posed a severe challenge to the security application of Global Navigation Satellite System (GNSS). It is particularly urgent and critical to carry out in-depth defense research on spoofing interference. When combined with the inertial navigation system (INS), the GNSS/INS integrated navigation system offers distinct advantages in the field of anti-spoofing technology research, which has garnered significant attention in recent years. To summarize the current research achievements of GNSS/INS integrated navigation anti-spoofing technology, it is necessary to provide an overview of the three core technical aspects of spoofing attack principles and implementation strategies, spoofing detection, and spoofing mitigation. First, the principles and implementation strategies of spoofing interference attacks are introduced, and different classifications of spoofing interference attacks are given. Then, the performance characteristics and technical points of different spoofing detection and spoofing mitigation methods are compared and analyzed, and the shortcomings and challenges in the current development of GNSS/INS anti-spoofing technology are pointed out. Finally, based on the summary and shortcomings of the existing technology, a prospect for the future development of GNSS/INS integrated navigation anti-spoofing technology is discussed.

Innovation Adaptive UKF Train Location Method Based on Kinematic Constraints

To address the issue of reduced positioning accuracy caused by satellite signal interruptions when trains pass through long tunnels, a novel train positioning method based on an innovative adaptive unscented Kalman filter (UKF) under kinematic constraints is proposed. This method aims to improve the accuracy of the location of trains during operation. By considering the dynamic characteristics of the train, a dynamic kinematic-constrained inertial navigation system (INS)/odometer (ODO) combination positioning system is established. This system utilizes kinematic constraints to correct the accumulated errors of the INS. Additionally, the algorithm incorporates real-time estimation of the measurement noise covariance using innovation sequences. The updated adaptive estimation algorithm is applied within the UKF framework for nonlinear filtering, forming the innovative adaptive UKF algorithm. At each time step, the difference between the ODO sensor data and the INS output is used as the measurement input for the innovative adaptive UKF algorithm, enabling global estimation. This process ultimately yields the actual positioning result for the train. Simulation results demonstrate that the innovative adaptive UKF train positioning method, incorporating kinematic constraints, effectively mitigates the impact of satellite signal interruptions. Compared with the traditional INS/ODO positioning method, the innovative adaptive UKF method reduces position errors by 34.35% and speed errors by 36.33%. Overall, this method enhances navigation accuracy, minimizes train positioning errors, and meets the requirements of modern train positioning systems.

Seal Pipeline: Enhancing Dynamic Object Detection and Tracking for Autonomous Unmanned Surface Vehicles in Maritime Environments

This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to enhance detection accuracy and reliability. The approach begins with an innovative preprocessing stage that integrates data from the Inertial Measurement Unit (IMU) with LiDAR sensors to correct tilt-induced distortions in LiDAR point cloud data and reduce ripple effects around objects. The adjusted data are grouped using clustering algorithms and bounding boxes for precise object localization. Additionally, a specialized Kalman filter tailored for maritime environments mitigates object discontinuities between successive frames and addresses data sparsity caused by boat tilting. The methodology was evaluated using the VRX simulator, with experiments conducted on the Volga River using real USVs. The preprocessing effectiveness was assessed using the Root Mean Square Error (RMSE) and tracking accuracy was evaluated through detection rate metrics. The results demonstrate a 25% to 30% improvement in detection accuracy and show that the pipeline can aid industry even with sparse object representation across different frames. This study highlights the potential of integrating sensor fusion with specialized tracking for accurate dynamic object detection in maritime settings, establishing a new benchmark for USV navigation systems’ accuracy and reliability.

Tunable Acoustic Tweezer System for Precise Three-Dimensional Particle Manipulation

This study introduces a tunable acoustic tweezer system designed for precise three-dimensional particle trapping and manipulation. The system utilizes a dual-liquid-layer acoustic lens, which enables the dynamic control of the focal length through the adjustable curvature of a latex membrane. This tunability is essential for generating the acoustic forces necessary for effective manipulation of particles, particularly along the direction of acoustic wave propagation (z-axis). Experiments conducted with spherical particles as small as 1.5 mm in diameter demonstrated the system’s capability for stable trapping and manipulation. Performance was rigorously evaluated through both z-axis and 3D manipulation tests. In the z-axis experiments, the system achieved a manipulation range of 33.4–53.4 mm, with a root-mean-square error and standard deviation of 0.044 ± 0.045 mm, which highlights its precision. Further, the 3D manipulation experiments showed that particles could be accurately guided along complex paths, including multilayer rectangular and helical trajectories, with minimal deviation. A visual feedback-based particle navigation system significantly enhanced positional accuracy, reducing errors relative to open-loop control. These results confirm that the tunable acoustic tweezer system is a robust tool for applications requiring precise control of particles with diameter of 1.5 mm in three-dimensional environments. Considering its ability to dynamically adjust the focal point and maintain stable trapping, this system is well suited for tasks demanding high precision, such as targeted particle delivery and other applications involving advanced material manipulation.

Advanced Edge AI Navigation and Assistance System for Blind User

- Despite substantial advancements in visual assistance technology, many existing systems are limited by sensor capabilities, computational resources, and power consumption. Traditional computer vision algorithms struggle to perform complex tasks required for real-time navigation. This paper presents the design and implementation of an advanced computer vision-based navigation system specifically tailored for Blender users, aimed at facilitating independent navigation in diverse environments .By leveraging edge Artificial Intelligence (AI) and deep learning methodologies, the proposed system achieves real-time object detection, person recognition, and environmental awareness, addressing the critical challenges posed by conventional systems. The use of cost-effective, low-power mobile computing platforms, such as smart depth sensors like the OpenCV AI Kit-Depth (OAK-D), enables efficient processing while ensuring portability. This system not only enhances the ability to identify and navigate obstacles but also incorporates additional functionalities, including reading written notices aloud and responding to traffic signals. Key design considerations, such as training data collection, computational efficiency, and portability, have been meticulously addressed to ensure reliable performance in real-world scenarios. The incorporation of an AI-driven voice interface facilitates user-friendly interaction, making this system an innovative and unobtrusive visual assistance device.

GNSS/IMU/LO integration with a new LO error model and lateral constraint for navigation in urban areas

The quest for reliable vehicle navigation in urban environments has led the integration of Light Detection and Ranging (LiDAR) Odometry (LO) with Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMU). However, the performance of the integrated system is limited by a lack of accurate LO error modeling. In this paper, we propose a weighted GNSS/IMU/LO integration-based navigation system with a novel LO error model. The Squared Exponential Gaussian Progress Regression (SE-GPR) based LO error model is developed by considering the vehicle velocity and number of point cloud features. Based on error prediction for GNSS positioning and LO, a weighting strategy is designed for integration in an Extended Kalman Filter (EKF). Furthermore, error accumulation of the navigation state, especially in GNSS-challenging scenarios, is restrained by the LiDAR-Aided Lateral Constraint (LALC) and Non-Holonomic Constraint (NHC). An experiment was conducted in a deep urban area to test the proposed algorithm. The results show that the proposed algorithm delivers horizontal and three-dimensional (3D) positioning Root Mean Square Errors (RMSEs) of 3.669 m and 5.216 m, respectively. The corresponding accuracy improvements are 35.9% and 50.0% compared to the basic EKF based GNSS/IMU/LO integration.