GNSS-challenged Environments Research Articles

Robust Direction Estimation of Terrestrial Signal via Sparse Non-Uniform Array Reconfiguration under Perturbations

DOA (Direction of Arrival), as an important observation parameter for accurately locating the Signals of Opportunity (SOP), is vital for navigation in GNSS-challenged environments and can be effectively obtained through sparse arrays. In practical application, array perturbations affect the estimation accuracy and stability of DOA, thereby adversely affecting the positioning performance of SOP. Against this backdrop, we propose an approach to reconstruct non-uniform arrays under perturbation conditions, aiming to improve the robustness of DOA estimation in sparse arrays. Firstly, we theoretically derive the mathematical expressions of the Cramér–Rao Bound (CRB) and Spatial Correlation Coefficient (SCC) for the uniform linear array (ULA) with perturbation. Then, we minimize CRB as the objective function to mitigate the adverse effects of array perturbations on DOA estimation, and use SCC as a constraint to suppress sidelobes. By doing this, the non-uniform array reconstruction model is formulated as a high-order 0–1 optimization problem. To effectively solve this nonconvex model, we propose a polynomial-time algorithm, which can converge to the optimal approximate solution of the original model. Finally, through a series of simulation experiments utilizing frequency modulation (FM) signal as an example, the exceptional performance of this method in array reconstruction has been thoroughly validated. Experimental data show that the reconstructed non-uniform array excels in DOA estimation accuracy compared to other sparse arrays, making it particularly suitable for estimating the direction of terrestrial SOP in perturbed environments.

An enhanced RANSAC-RTK algorithm in GNSS-challenged environments

ABSTRACT Outliers can significantly impact the accuracy and reliability of Global Navigation Satellite System (GNSS) real-time kinematic (RTK) positioning. To enhance the robustness of RTK in GNSS-challenged environments, we propose an enhanced random sample consensus RTK (RANSAC-RTK) algorithm capable of effectively handling multiple and continuous outliers. The enhancements to the RANSAC algorithm include threshold setting, pre-screening samples and sample verification tailored to the characteristics of GNSS data. The experimental results indicate that the standard RTK algorithm is vulnerable to outliers. By contrast, the enhanced RANSAC-RTK algorithm can effectively handle multiple and continuous outliers, resulting in 33% increase in the ambiguity fixing rate and 15% improvement in positioning accuracy.

LEO-Enhanced GNSS/INS Tightly Coupled Integration Based on Factor Graph Optimization in the Urban Environment

Precision point positioning (PPP) utilizing the Global Navigation Satellite System (GNSS) is a traditional and widely employed technology. Its performance is susceptible to observation discontinuities and unfavorable geometric configurations. Consequently, the integration of the Inertial Navigation System (INS) and GNSS makes full use of their respective advantages and effectively mitigates the limitations of GNSS positioning. However, the GNSS/INS integration faces significant challenges in complex and harsh urban environments. In recent years, the geometry between the user and the satellite has been effectively improved with the advent of lower-orbits and faster-speed Low Earth Orbit (LEO) satellites. This enhancement provides more observation data, opening up new possibilities and opportunities for high-precision positioning. Meanwhile, in contrast to the traditional extended Kalman filter (EKF) approach, the performance of the LEO-enhanced GNSS/INS tightly coupled integration (TCI) can be significantly improved by employing the factor graph optimization (FGO) method with multiple iterations to achieve stable estimation. In this study, LEO data and the FGO method were employed to enhance the GNSS/INS TCI. To validate the effectiveness of the method, vehicle data and simulated LEO observations were subjected to thorough analysis. The results suggest that the integration of LEO data significantly enhances the positioning accuracy and convergence speed of the GNSS/INS TCI. In contrast to the FGO GNSS/INS TCI without LEO enhancement, the average enhancement effect of the LEO is 22.16%, 7.58%, and 10.13% in the north, east, and vertical directions, respectively. Furthermore, the average root mean square error (RMSE) of the LEO-enhanced FGO GNSS/INS TCI is 0.63 m, 1.21 m, and 0.85 m in the north, east, and vertical directions, respectively, representing an average improvement of 41.91%, 13.66%, and 2.52% over the traditional EKF method. Meanwhile, the simulation results demonstrate that LEO data and the FGO method effectively enhance the positioning and convergence performance of GNSS/INS TCI in GNSS-challenged environments (tall buildings, viaducts, underground tunnels, and wooded areas).

Improved Information Fusion for Agricultural Machinery Navigation Based on Context-Constrained Kalman Filter and Dual-Antenna RTK

Automatic navigation based on dual-antenna real-time kinematic (RTK) positioning has been widely employed for unmanned agricultural machinery, whereas GNSS inevitably suffers from signal blocking and electromagnetic interference. In order to improve the reliability of an RTK-based navigation system in a GNSS-challenged environment, an integrated navigation system is preferred for autonomous navigation, which increases the complexity and cost of the navigation system. The information fusion of integrated navigation has been dominated by Kalman filter (KF) for several decades, but the KF cannot assimilate the known knowledge of the navigation context efficiently. In this paper, the geometric characteristics of the straight path and path-tracking error were employed to formulate the constraint measurement model, which suppresses the position error in the case of RTK-degraded scenarios. The pseudo-measurements were then imported into the KF framework, and the smoothed navigation state was generated as a byproduct, which improves the reliability of the RTK positioning without external sensors. The experiment result of the mobile vehicle automatic navigation indicates that the tracking error-constrained KF (EC-KF) outperforms the trajectory-constrained KF (TC-KF) and KF when the RTK system outputs a float or single-point position (SPP) solution. In the case where the duration of the SPP solution was 20 s, the positioning errors of the EC-KF and TC-KF were reduced by 38.50% and 24.04%, respectively, compared with those of the KF.

Adaptive-Robust Fusion Strategy for Autonomous Navigation in GNSS-Challenged Environments

Adaptive-Robust Fusion Strategy for Autonomous Navigation in GNSS-Challenged Environments

Observability-Constrained Resampling-Free Cubature Kalman Filter for GNSS/INS with Measurement Outliers

Integrating global navigation satellite systems (GNSSs) with inertial navigation systems (INSs) has been widely recognized as an ideal solution for autonomous vehicle navigation. However, GNSSs suffer from disturbances and signal blocking inevitably, making the performance of GNSS/INSs degraded in the occurrence of measurement outliers. It has been proven that the sigma points-based Kalman filter (KF) performs better than an extended KF in cases where large prior uncertainty is present in the state estimation of a GNSS/INS. By modifying the sigma points directly, the resampling-free sigma point update framework (SUF) propagates more information excepting Gaussian moments of prescribed accuracy, based on which the resampling-free cubature Kalman filter (RCKF) was developed in our previous work. In order to enhance the adaptivity and robustness of the RCKF, the resampling-free SUF depending on dynamic prediction residue should be improved by suppressing the time-varying measurement outlier. In this paper, a robust observability-constrained RCKF (ROCRCKF) is proposed based on adaptive measurement noise covariance estimation and outlier detection, where the occurrence of measurement outliers is modelled by the Bernoulli variable and estimated with the state simultaneously. Experiments based on car-mounted GNSS/INSs are performed to verify the effectiveness of the ROCRCKF, and result indicates that the proposed algorithm outperforms the RCKF in the presence of measurement outliers, where the heading error and average root mean square error of the position are reduced from 1.96° and 6.38 m to 0.27° and 5.95 m, respectively. The ROCRCKF is robust against the measurement outliers and time-varying model uncertainty, making it suitable for the real-time implementation of GNSS/INSs in GNSS-challenged environments.

Tightly coupled multi-frequency PPP-RTK/INS integration model and its application in an urban environment

ABSTRACT The Precise Point Positioning-Real Time Kinematic (PPP-RTK) technique, which provides centimeter-level positioning with instantaneous ambiguity resolution, is considered as a potential tool for intelligent vehicle applications. However, its performance is restricted under complex urban conditions owing to intermittent signal interruptions and poor satellite geometries. Thus, a tightly coupled Multi-Frequency (MF) PPP-RTK/INS (Inertial Navigation System) model was developed with the objective of providing a stable and reliable positioning for the urban vehicle. In this model, the augmentation of INS information, third-frequency observations, precise atmospheric corrections, the fixed Extra-Wide Lane (EWL), and Wide-Lane (WL) ambiguities can be used to enhance the positioning performance of PPP-RTK. We designed urban vehicle experiments under different scenarios to validate the proposed method. The results show that PPP-RTK can be significantly improved for urban vehicle positioning by fusing the MF and INS. In urban areas, the solution availability with a horizontal positioning error within 10 cm was 96.1% for MFPPP-RTK/INS with a fixing percentage of 90.9%. Compared with the dual-frequency PPP-RTK solutions, the fixing percentage and solution availability in the MFPPP-RTK/INS was improved by 9.5% and 8.8%, respectively. Moreover, MFPPP-RTK/INS provides continuous and stable positioning and fast ambiguity recovery in GNSS-challenged environments. The MFPPP-RTK/INS could achieve a fast ambiguity re-fixing within 1 s after continuously crossing obstacles, whereas PPP-RTK could achieve the same in 10 s.

Enhancing RTK Performance in Urban Environments by Tightly Integrating INS and LiDAR

High-precision and continuous positioning is a fundamental requirement for intelligent navigation applications. Nowadays, the global navigation satellite system (GNSS) real-time kinematic (RTK) technique is recognized as a feasible solution to provide precise positioning services, but its accuracy is susceptible to signal attenuation and will deteriorate drastically in urban environments. Fortunately, the low-cost inertial measurement units (IMU) and light detection and ranging (LiDAR) are available in modern vehicle systems and could be integrated to enhance GNSS performance. In this contribution, aiming to improve RTK performance in GNSS-challenging environments, we propose a tightly coupled RTK/Inertial navigation system (INS)/LiDAR integration method. The GNSS double-differenced pseudorange and carrier-phase measurements, IMU data, and LiDAR plane features are fused at the raw-data level via an extended Kalman filter (EKF). Both simulated tests and real-world experiments were conducted to evaluate the effectiveness of the proposed method. The results indicate that the proposed method is able to achieve sub-decimeter-level accuracy in GNSS-challenging environments, with the improvements of (51.8%, 82.0%, 75.0%) and (53.9%, 71.0%, 41.5%) compared to state-of-the-art LIO-SAM and loosely coupled GNSS/INS/LiDAR integration. Meanwhile, the ambiguity fixing rate could be improved by more than 10% with the assistance of LiDAR plane features. Similar improvements can also be achieved in velocity and attitude estimation.

Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments.

This research develops an integrated navigation system, which fuses the measurements of the inertial measurement unit (IMU), LiDAR, and monocular camera using an extended Kalman filter (EKF) to provide accurate positioning during prolonged GNSS signal outages. The system features the use of an integrated INS/monocular visual simultaneous localization and mapping (SLAM) navigation system that takes advantage of LiDAR depth measurements to correct the scale ambiguity that results from monocular visual odometry. The proposed system was tested using two datasets, namely, the KITTI and the Leddar PixSet, which cover a wide range of driving environments. The system yielded an average reduction in the root-mean-square error (RMSE) of about 80% and 92% in the horizontal and upward directions, respectively. The proposed system was compared with an INS/monocular visual SLAM/LiDAR SLAM integration and to some state-of-the-art SLAM algorithms.

Radar/INS Integration and Map Matching for Land Vehicle Navigation in Urban Environments.

Autonomous navigation requires multi-sensor fusion to achieve a high level of accuracy in different environments. Global navigation satellite system (GNSS) receivers are the main components in most navigation systems. However, GNSS signals are subject to blockage and multipath effects in challenging areas, e.g., tunnels, underground parking, and downtown or urban areas. Therefore, different sensors, such as inertial navigation systems (INSs) and radar, can be used to compensate for GNSS signal deterioration and to meet continuity requirements. In this paper, a novel algorithm was applied to improve land vehicle navigation in GNSS-challenging environments through radar/INS integration and map matching. Four radar units were utilized in this work. Two units were used to estimate the vehicle's forward velocity, and the four units were used together to estimate the vehicle's position. The integrated solution was estimated in two steps. First, the radar solution was fused with an INS through an extended Kalman filter (EKF). Second, map matching was used to correct the radar/INS integrated position using OpenStreetMap (OSM). The developed algorithm was evaluated using real data collected in Calgary's urban area and downtown Toronto. The results show the efficiency of the proposed method, which had a horizontal position RMS error percentage of less than 1% of the distance traveled for three minutes of a simulated GNSS outage.

THE DEVELOPMENT AND VALIDATION OF A NAVIGATION GRADE EGI SYSTEM FOR LAND VEHICULAR NAVIGATION APPLICATIONS

Abstract. In recent years, most people use commercial integrated navigation systems to develop navigation algorithms. However, due to the different levels of sensors on the market, it is difficult to customize commercial systems and leads to limited development of navigation algorithms. Therefore, the purpose of this research is to develop a real-time integrated navigation system EGI-1000 (Embedded GNSS and INS) including software and hardware, and effectively reduce the cost with the commercial price. The real-time integrated navigation system EGI-1000 contains a navigation-grade IMU, IMU1000 and NovAtel OEM 7720 GNSS receiver module. In this research, the integration process can be divided into three parts. The first part is the integration of hardware, and the architecture diagram of the real-time integrated navigation system will be displayed. The second part is the pre-processing of data. In the multi-sensor time synchronization problem, this research will propose a method about cross-correlation to validate whether the timestamp of IMU data is delay. The last part is algorithm about fusing data from multiple sensors and motion constraints. Extended Kaman Filter (EKF) will be the core and motion constraints including Zero Velocity Update (ZUPT) and Non-Holonomic Constraints (NHC) are integrated in the Loosely Coupled (LC) scheme. The calibration of Inertial navigation Measurement Unit (IMU) will also be conducted to determine the parameter in algorithm. The results of the experiments will be shown in this paper. Both of hardware and navigation algorithm in the integrated navigation system of this research are used to conduct multiple experiment including open sky environments, GNSS challenging environments, and GNSS denied environment. In comparison with the reference data, the navigation accuracy of the developed integrated navigation system can achieve centimeter-level accuracy (“Active Control” level and “Where in lane” level) in open sky and GNSS challenging environments. According to the propagation error theory, the result in GNSS denied environment also meet the expected value. The navigation algorithm is also feasible for the commercial integrated navigation system.

Fusion of GNSS Pseudoranges with UWB Ranges Based on Clustering and Weighted Least Squares.

Global navigation satellite systems (GNSSs) and ultra-wideband (UWB) ranging are two central research topics in the field of positioning and navigation. In this study, a GNSS/UWB fusion method is investigated in GNSS-challenged environments or for the transition between outdoor and indoor environments. UWB augments the GNSS positioning solution in these environments. GNSS stop-and-go measurements were carried out simultaneously to UWB range observations within the network of grid points used for testing. The influence of UWB range measurements on the GNSS solution is examined with three weighted least squares (WLS) approaches. The first WLS variant relies solely on the UWB range measurements. The second approach includes a measurement model that utilizes GNSS only. The third model fuses both approaches into a single multi-sensor model. As part of the raw data evaluation, static GNSS observations processed with precise ephemerides were used to define the ground truth. In order to extract the grid test points from the collected raw data in the measured network, clustering methods were applied. A self-developed clustering approach extending density-based spatial clustering of applications with noise (DBSCAN) was employed for this purpose. The results of the GNSS/UWB fusion approach show an improvement in positioning performance compared to the UWB-only approach, in the range of a few centimeters to the decimeter level when grid points were placed within the area enclosed by the UWB anchor points. However, grid points outside this area indicated a decrease in accuracy in the range of about 90 cm. The precision generally remained within 5 cm for points located within the anchor points.

Resilient Smartphone Positioning Using Native Sensors and PPP Augmentation

<h3>Abstract</h3> With the ubiquitous use of global navigation satellite system (GNSS) receivers, navigation solutions from smartphones have become integrated in various applications throughout our lives. These ultra-low-cost GNSS receivers have the drawbacks of insufficient observations and poorer signal reception quality than higher-cost receivers. Since 2016, smartphones using the Android operating system have been able to output raw GNSS pseudorange and carrier-phase measurements, thereby enabling improved navigation capabilities. The realm of sensor fusion is also being explored by using smartphone sensors, including inertial measurement units (IMUs), cameras, and other fusion techniques. The research presented herein deployed only IMU and GNSS sensors native to existing smartphones and achieved a standalone solution using PPP/IMU integration that outperformed standard techniques. In open-sky vehicle experiments, the sensor integration algorithm achieved 1.6-m horizontal RMS, thus reducing 80% of horizontal errors in GNSS-challenging environments through a tightly coupled GNSS-PPP solution that is yet to appear in publications.

A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging Environments.

Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying solely on GNSS might pose safety risks in the event of receiver malfunction or antenna installation error. In this research, an unmanned aerial system (UAS) employing the Applanix APX15 GNSS/IMU board, a Velodyne Puck LiDAR sensor, and a Sony a7R II high-resolution camera was used to collect data for the purpose of developing a multi-sensor integration system. Unfortunately, due to a malfunctioning GNSS antenna, there were numerous prolonged GNSS signal outages. As a result, the GNSS/INS processing failed after obtaining an error that exceeded 25 km. To resolve this issue and to recover the precise trajectory of the UAV, a GNSS/INS/LiDAR integrated navigation system was developed. The LiDAR data were first processed using the optimized LOAM SLAM algorithm, which yielded the position and orientation estimates. Pix4D Mapper software was then used to process the camera images in the presence of ground control points (GCPs), which resulted in the precise camera positions and orientations that served as ground truth. All sensor data were timestamped by GPS, and all datasets were sampled at 10 Hz to match those of the LiDAR scans. Two case studies were considered, namely complete GNSS outage and assistance from GNSS PPP solution. In comparison to the complete GNSS outage, the results for the second case study were significantly improved. The improvement is described in terms of RMSE reductions of approximately 51% and 78% for the horizontal and vertical directions, respectively. Additionally, the RMSE of the roll and yaw angles was reduced by 13% and 30%, respectively. However, the RMSE of the pitch angle was increased by about 13%.

Tightly Coupled Integration of GNSS, INS, and LiDAR for Vehicle Navigation in Urban Environments

The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, the global navigation satellite system (GNSS) is recognized as an important approach for worldwide positioning services. However, its application is limited in urban areas due to severe signal attenuation, reflections, and blockages. Inertial navigation system (INS) can provide high-precision navigation outputs within a short period, but its accuracy suffers from error accumulation, especially when equipped with the low-cost microelectromechanical system (MEMS) inertial measurement units (IMUs). In addition, light detection and ranging (LiDAR) is becoming more common as an option in vehicles, which can detect rich geometric information in the environment for ego-motion estimation. Aiming at taking advantage of the complementary characteristics of these onboard technologies to navigate in urban environments, a tightly coupled multi-GNSS precise point positioning (PPP)/INS/LiDAR integrated system is proposed. We also develop an LiDAR sliding-window plane-feature tracking method to further improve navigation accuracy and computational efficiency. The performance of the proposed integrated system was evaluated in vehicular experiments with different GNSS observation conditions. Results indicate that our proposed GNSS/INS/LiDAR integration can maintain submeter level horizontal positioning accuracy in GNSS-challenging environments, with improvements of (73.3%, 59.7%, and 64.2%) compared to traditional GNSS/INS integration. Moreover, the plane-feature tracking method is proved to outperform traditional point-to-line and point-to-plane scan matching in terms of accuracy and efficiency.

Closed loop integration of air-to-air visual measurements for cooperative UAV navigation in GNSS challenging environments

This paper discusses the integration of air-to-air visual measurements within a cooperative multi-vehicle architecture conceived to improve navigation performance of small Unmanned Aerial Vehicles in GNSS-challenging environments. The key concept is to exploit cooperation with other aircraft flying under better GNSS coverage conditions by exchanging navigation data and using a monocular camera system for relative sensing purposes. Specifically, accurate line of sight, obtained using Deep Learning-based detectors and local image analysis, are complemented with distance information achieved through an innovative shape-based passive ranging approach accounting for both the target attitude and its position in the field of view. Camera-based measurements are combined with the estimates generated by the other onboard sensors, within a customized Extended Kalman Filter in a closed loop fashion, since navigation estimates are used in feedback as hints for visual processing. An experimental flight test campaign is carried out using two quadcopters. A comparison between filter performance achievable as a function of the specific set of available information sources, i.e., bearing-only vs. line of sight and ranging, is carried out. Results show that the trade-off between correct, false, and missed detections, as well as the passive ranging accuracy allow the filter ensuring metric-level positioning error within GNSS-challenging areas. The added value of using both bearing and range measurements strongly depends on the formation geometry and the GNSS coverage conditions and can be predicted thanks to the “generalized dilution of precision”.

An Aerial and Ground Multi-Agent Cooperative Location Framework in GNSS-Challenged Environments

In order to realize the cooperative localization of multi-unmanned platforms in the GNSS-denied environment, this paper proposes a collaborative SLAM (simultaneous localization and mapping, SLAM) framework based on image feature point matching. Without GNSS, a single unmanned platform UGV and UAV (unmanned ground vehicle, UGV; unmanned aerial vehicle, UAV) equipped with vision and IMU (inertial measurement unit, IMU) sensors can exchange information through data communication to jointly build a three-dimensional visual point map, and determine the relative position of each other through visual-based position re- identification and PnP (Perspective-n-Points, PnP) methods. When any agent can receive reliable GNSS signals, GNSS positioning information will greatly improve the positioning accuracy without changing the positioning algorithm framework. In order to achieve this function, we designed a set of two-stage position estimation algorithms. In the first stage, we used the modified ORB-SLAM3 algorithm for position estimation by fusing visual and IMU information. In the second stage, we integrated GNSS positioning and cooperative positioning information using the factor graph optimization (FGO) algorithm. Our framework consists of an UGV as the central server node and three UAVs carried by the UGV, that will collaborate on space exploration missions. Finally, we simulated the influence of different visibility and lighting conditions on the framework function on the virtual simulation experiment platform built based on ROS (robot operating system, ROS) and Unity3D. The accuracy of the cooperative localization algorithm and the single platform localization algorithm was evaluated. In the two cases of GNSS-denied and GNSS-challenged, the error of co-location reduced by 15.5% and 19.7%, respectively, compared with single-platform independent positioning.

Integration of Multi-GNSS PPP-RTK/INS/Vision with a Cascading Kalman Filter for Vehicle Navigation in Urban Areas

Precise point positioning (PPP) has received much attention in recent years for its low cost, high accuracy, and global coverage. Nowadays, PPP with ambiguity resolution and atmospheric augmentation is widely regarded as PPP-RTK (real-time kinematic), which weakens the influence of the long convergence time in PPP and regional service coverage in RTK. However, PPP-RTK cannot work well in urban areas due to limitations of non-line-of-sight (NLOS) conditions. Inertial navigation systems (INS) and vision can realize continuous navigation but suffer from error accumulation. Accordingly, the integration model of multi-GNSS (global navigation satellite system) and PPP-RTK/INS/vision with a cascading Kalman filter and dynamic object removal model was proposed to improve the performance of vehicle navigation in urban areas. Two vehicular tests denoted T01 and T02 were conducted in urban areas to evaluate the navigation performance of the proposed model. T01 was conducted in a relatively open-sky environment and T02 was collected in a GNSS-challenged environment with many obstacles blocking the GNSS signals. The positioning results show that the dynamic object removal model can work well in T02. The results indicate that multi-GNSS PPP-RTK/INS/vision with a cascading Kalman filter can achieve a positioning accuracy of 0.08 m and 0.09 m for T01 in the horizontal and vertical directions and 0.83 m and 0.91 m for T02 in the horizontal and vertical directions, respectively. The accuracy of the velocity and attitude estimations is greatly improved by the introduction of vision.

Multifusion schemes of INS/GNSS/GCPs/V-SLAM applied using data from smartphone sensors for land vehicular navigation applications

Contemporary smartphones contain embedded inertial measurement unit (IMU), global navigation satellite system (GNSS), camera, and other sensors that are capable of providing user position, velocity, and attitude. However, the actual navigation performance capabilities of smartphones are difficult to use because of the low-cost and disparate sensors employed, differing software technologies adopted by manufacturers, and considerable influence of environmental conditions. In this study, we proposed multifusion schemes that integrated sensor data from smartphone IMU, GNSS chipsets, cameras, and ground control points (GCPs), using an extended Kalman filter to enhance the system navigation performance. Different processes of scale recovery and refreshed-simultaneous localization and mapping (SLAM) based on GNSS and GCPs corresponding to outdoor and indoor environments were proposed to increase the accuracy and robustness of the integrated system. To verify the performance of the integrated system, field test data were collected in an urban area of Tainan City, Taiwan. The experimental results indicated improvements of 87.09% and 36.27% for the refreshed-SLAM and its integration system, respectively, compared with visual-SLAM one-scale recovery and conventional integrated schemes. The proposed integrated system that uses smartphone sensor data increased navigation accuracy in GNSS-challenged environments.

An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments.

Traditional navigation systems rely on GNSS/inertial navigation system (INS) integration, in which the INS can provide reliable positioning during short GNSS outages. However, if the GNSS outage persists for prolonged periods of time, the performance of the system will be solely dependent on the INS, which can lead to a significant drift over time. As a result, the need to integrate additional onboard sensors is essential. This study proposes a robust loosely coupled (LC) integration between the INS and LiDAR simultaneous mapping and localization (SLAM) using an extended Kalman filter (EKF). The proposed integrated navigation system was tested for three different driving scenarios and environments using the raw KITTI dataset. The first scenario used the KITTI residential datasets, totaling 48 min, while the second case study considered the KITTI highway datasets, totaling 7 min. For both case studies, a complete absence of the GNSS signal was assumed for the whole trajectory of the vehicle in all drives. In contrast, the third case study considered the use of minimal assistance from GNSS, which mimics the intermittent receipt and loss of GNSS signals for different driving environments. The positioning results of the proposed INS/LiDAR SLAM integrated system outperformed the performance of the INS for the residential datasets with an average reduction in the root mean square error (RMSE) in the horizontal and up directions of 88% and 32%, respectively. For the highway datasets, the RMSE reductions were 70% and 0.2% for the horizontal and up directions, respectively.