In recent years, wearable sensors and mobile devices have become popular tools in the field of positioning. In pedestrian navigation, Pedestrian Dead Reckoning (PDR) is the primary algorithm, with numerous previous research cases available. However, traditional PDR algorithms' stride length calculations rely on empirical formulas that are influenced by factors such as user height, walking frequency, and walking habits. Without appropriate parameters, stride length estimation can result in significant errors, leading to poor positioning outcomes. Apart from the stride length calculation issue, IMUs contain bias and noise themselves, leading to drift errors over time, especially in consumer-grade IMUs. To address these issues, this study introduces a CNN velocity estimation model to calculate users' 1D velocity. The trained velocity estimation model can overcome the user dependency issue caused by traditional PDR because the training data covers different users. For the heading calculation, this study employs a novel 9D IMU AHRS algorithm (Laidig et al., 2022) to address attitude estimation problems that traditional PDR cannot handle effectively under high motion conditions. Finally, incorporating GNSS through the principles of Extended Kalman Filter(EKF) to compensate for IMU’s drift over time. In the experiment, we use NovAtel Pwrpak as ground truth. It contains high-quality GNSS and IMU, which can provide reliable reference trajectory. A comparison of trajectories is conducted using Huawei mate20 pro as our smartphone device in different modes.

High-definition (HD) maps have recently become a key piece of technology in autonomous driving. Over the past few years, various methods and sensors, such as those based on inertial navigation system (INS), global navigation satellite system (GNSS), cameras, and light detection and ranging (LiDAR), have been used to develop HD maps. In this study, we developed novel techniques for enhancing the creation and verification of HD point cloud maps. First, a tightly coupled (TC) INS/GNSS-assisted 3-D normal distribution transform (NDT)-LiDAR mapping system has been developed. Utilizing an integrated INS/GNSS, the system provides a reliable initial pose, thereby mitigating the issue of divergence in NDT scan matching, particularly when the vehicle operates at high speeds in challenging LiDAR environments. This approach enhances both navigation accuracy and the precision of the point cloud map. Second, alternative ground control points (GCPs) have been established as substitutes for conventional techniques, addressing freeway regulations and managing safety concerns. Third, to ensure the desired accuracy for “where-in-lane” positioning in autonomous vehicle applications, the created point cloud map was validated against the criteria outlined by standardized procedures. Overall, our preliminary results indicate that our HD point cloud map meets the positioning accuracy criteria outlined by the Taiwan Association of Information and Communication Standards. Our point density results also indicate that our generated point cloud map can achieve a high degree of accuracy in in-lane positioning for autonomous vehicle navigation.

The advancement of microelectromechanical systems (MEMSs) and the Internet of Things (IoT) have enabled a wide range of applications based on smartphones. However, the existing navigation methods using these low-cost MEMS sensors cannot provide acceptable information for location-based applications in various environments. Their technical limitations, such as severe signal attenuation, reflections, blockages, error accumulation, and low quality of images degrade the performance of global navigation satellite system (GNSS), inertial navigation system (INS), and camera. To mitigate these limitations, especially in indoor vehicle navigation, we first analyze the performance of the existing fusion algorithm, then we propose semantic proximity update (SPU) based on a pretrained model of real-time object detection to enhance the integration of GNSS, INS, and visual INS (VINS). SPU consists of the detection of geo-referenced objects and the relative movement to infer the absolute position. The proposed INS/GNSS/VINS/SPU can maintain long-term acceptable accuracy regardless of the indoor/outdoor environment. It only requires the use of smartphone sensors; thus, this scheme has no additional cost for users. Experimental results indicated that the errors of this scheme in horizontal positioning and 3-D positioning were 51.6% and 86.8% lower, respectively, than those of a conventional integration.

Positioning, navigation, and timing (PNT) is essential for Internet of Things (IoT) communications and location-based services. Although global navigation satellite system (GNSS) can provide accurate PNT in open areas, obtaining reliable PNT is still a considerable technical challenge in complex urban environments. This is because the GNSS signals are more likely to be affected by multipath interference and nonline of sight (NLOS) reception issues arising from the obstructions and reflections in built environments. These introduce range measurement errors that degrade the GNSS positioning accuracy. This article proposes two resilient pseudorange error prediction and correction strategies to improve the GNSS positioning accuracy in urban environments. In particular, considering the carrier-to-noise density ($C/N$textsubscript 0), satellite elevation angle, and local positional information, the random forest-based pseudorange error prediction and correction models are constructed in two variations, including: 1) the point-based correction (PBC) and 2) the grid-based correction (GBC). The final improved positioning solution is then calculated by using the least square method (LSM) of the corrected pseudoranges. Kinematic test results in urban environments show that both variations of the proposed model can improve the positioning accuracy by 42.9% and 40.8% in horizontal, and by 60.1% and 63.3% in 3-D, respectively, compared to the positioning results obtained by the traditional method without pseudorange error corrections. The improvements are 41.1% and 38.9% in horizontal, and 45.7% and 50.0% in 3-D, respectively, compared with traditional elevation angle weighting method.

To achieve a higher level of automation for modern development in automated driving systems (ADS), reliable evaluation of navigation states estimation is crucial demand. Although the presence of several approaches on evaluation are presented, but no study has examined problems related to establish a trustable reference system for fully evaluating performance of ADS. This paper proposes new strategies for better handling with the ground truth system for full navigation evaluation with automated driving applications. The first strategy involves making use of the integration solutions of an inertial measurement unit (IMU) and global navigation satellite system (GNSS) as an initial pose for normal distribution transform (NDT) with high-definition (HD) point cloud map. An accurate LiDAR-based navigation estimation could be then achieved. In the second strategy, LiDAR-based position is used as the measurements to update with the loosely coupled (LC)INS/GNSS/LiDAR integration system. The preliminary results indicate that the proposed LC-INS/GNSS/LiDAR strategy not only estimates full navigation solutions, but also seems to provide more accurate and reliable for evaluating the positioning, navigation and timing (PNT) services compared to conventional methods.

The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange-based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3-D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.

In disaster management, reconstructing the environment and quickly collecting the geospatial data of the impacted areas in a short time are crucial. In this letter, a light detection and ranging (LiDAR)-based unmanned aerial vehicle (UAV) is proposed to complete the reconstruction task. The UAV integrate an inertial navigation system (INS), a global navigation satellite system (GNSS) receiver, and a low-cost LiDAR. An unmanned helicopter is introduced and the multisensor payload architecture for direct georeferencing is designed to improve the capabilities of the vehicle. In addition, a new strategy of iterative closest point algorithm is proposed to solve the registration problems in the sparse and inhomogeneous derived point cloud. The proposed registration algorithm addresses the local minima problem by the use of direct-georeferenced points and the novel hierarchical structure as well as taking the feedback bias into INS/GNSS. The generated point cloud is compared with a more accurate one derived from a high-grade terrestrial LiDAR which uses real flight data. Results indicate that the proposed UAV system achieves meter-level accuracy and reconstructs the environment with dense point cloud.

An airborne gravimetry system consisting of an inertial navigation system (INS) and a global navigation satellite system (GNSS) has been proven to perform well in gravity observation. The system is also more cost- or time-effective than satellite missions and terrestrial gravimeters. In this letter, an unmanned aerial vehicle has been developed as a platform to carry the INS/GNSS vector gravimetry system using an unmanned helicopter. In addition to the kinematic mode, the unmanned helicopter can perform the zero velocity update (ZUPT) mode, which is a novel method in the acquisition of gravity. Results show that the accuracies of the horizontal and vertical gravity disturbance from the kinematic mode at crossover points are approximately 6-11 and 4 mGal, respectively, with a 0.5-km resolution. The accuracy of the repeatability in ZUPT mode is evaluated with the accuracies of approximately 2-3 mGal.

With the great international popularity and various sensors embedded, smartphone becomes an excellent mobile and indoor navigator. Pedestrian Dead Reckoning (PDR) is one of the most common technologies for pedestrian and indoor navigation which is based upon pedometer and orientation sensor. But various errors tend to accumulated step by step in its present form. Therefore, this study proposes a novel map aided Fuzzy Decision Tree (FDT) without complex algorithm and individually tuning process to reduce the accumulated error, improve the generation ability and minimize the use of infrastructure. The rule-based FDT algorithm estimates the location based upon the map, sensors and expert knowledge after training once then for other new experiments. Various scenarios consisted of different test sites, smartphones and users are implemented in order to verify the performance of proposed algorithm. The results verify that once the proposed algorithm is well trained, it is able to maintain good position performance regardless of the users, fields and smartphones. In addition, the positioning solution can output the global coordinate because of the use of self-produced map for seamless navigation in both indoor and outdoor environments.