A LiDAR-Inertial SLAM Tightly-Coupled with Dropout-Tolerant GNSS Fusion for Autonomous Mine Service Vehicles

Multi-modal sensor integration has become a crucial prerequisite for the real-world navigation systems. Recent studies have reported successful deployment of such system in many fields. However, it is still challenging for navigation tasks in mine scenes due to satellite signal dropouts, degraded perception, and observation degeneracy. To solve this problem, we propose a LiDAR-inertial odometry method in this paper, utilizing both Kalman filter and graph optimization. The front-end consists of multiple parallel running LiDAR-inertial odometries, where the laser points, IMU, and wheel odometer information are tightly fused in an error-state Kalman filter. Instead of the commonly used feature points, we employ surface elements for registration. The back-end construct a pose graph and jointly optimize the pose estimation results from inertial, LiDAR odometry, and global navigation satellite system (GNSS). Since the vehicle has a long operation time inside the tunnel, the largely accumulated drift may be not fully by the GNSS measurements. We hereby leverage a loop closure based re-initialization process to achieve full alignment. In addition, the system robustness is improved through handling data loss, stream consistency, and estimation error. The experimental results show that our system has a good tolerance to the long-period degeneracy with the cooperation different LiDARs and surfel registration, achieving meter-level accuracy even for tens of minutes running during GNSS dropouts

Four years of multi-modal odometry and mapping on the rail vehicles

Precise, seamless, and efficient train localization as well as long-term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety (RAMS) engineering for railroad systems. Simultaneous localization and mapping (SLAM) is right at the core of solving the two problems concurrently. In this end, we propose a high-performance and versatile multi-modal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation and map-based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors IMU and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared to point-only LiDAR-inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The Visual-inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long-during railway environments over four years, including general-speed, high-speed and metro, both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experience, problems, and successes of our group with the robotics community so that those that work in such environments can avoid these errors. In this view, we open source some of the datasets to benefit the research community

Quasi-4-dimension ionospheric modeling and its application in PPP

The version of record of this article, first published in Satellite Navigation, is available online at Publisher’s website: http://dx.doi.org/10.1186/s43020-022-00085-zIonospheric delay modeling is not only important for GNSS based space weather study and monitoring, but also an efficient tool to overcome the long convergence time of PPP. In this study, a novel model, denoted as Q4DIM (Quasi-4-dimension ionospheric modeling) is proposed for wide-area high precision ionospheric delay correction. In Q4DIM, the LOS (line of sight) ionospheric delay from a GNSS station network is divided into different clusters according to not only latitude and longitude, but also elevation and azimuth. Both GIM (global ionosphere map) and SID (slant ionospheric delay) that traditionally used for wide-area and regional ionospheric delay modeling, respectively, can be regarded as special case of Q4DIM by defining proper grids in latitude, longitude, elevation and azimuth. Thus, Q4DIM presents a resilient model that is capable for both wide-area coverage and high precision. Then four different sets of clusters are defined to illustrate the properties of Q4DIM based on 200 EPN stations. The results suggested that Q4DIM is compatible with the widely acknowledged GIM products. Moreover, it is proved that by inducting the elevation and azimuth angle dependent residuals, the precision of the 2-dimensional GIM-like model, i.e., Q4DIM-2D, is improved from around 1.5 TECU to better than 0.5 TECU. In addition, by treating Q4DIM as a 4-dimensional matrix in latitude, longitude, elevation and azimuth, its sparsity is less than 5%, thus guarantees its feasibility in a bandwidth-sensitive applications, e.g., satellite-based PPP-RTK service. Finally, the advantage of Q4DIM in single frequency PPP over the 2-dimensional models is demonstrated with one month’s data from 30 EPN stations.Peer ReviewedPostprint (published version

Comparing Monofractal and Multifractal Analysis of Corrosion Damage Evolution in Reinforcing Bars

Based on fractal theory and damage mechanics, the aim of this paper is to describe the monofractal and multifractal characteristics of corrosion morphology and develop a new approach to characterize the nonuniform corrosion degree of reinforcing bars. The relationship between fractal parameters and tensile strength of reinforcing bars are discussed. The results showed that corrosion mass loss ratio of a bar cannot accurately reflect the damage degree of the bar. The corrosion morphology of reinforcing bars exhibits both monofractal and multifractal features. The fractal dimension and the tensile strength of corroded steel bars exhibit a power function relationship, while the width of multifractal spectrum and tensile strength of corroded steel bars exhibit a linear relationship. By comparison, using width of multifractal spectrum as multifractal damage variable not only reflects the distribution of corrosion damage in reinforcing bars, but also reveals the influence of nonuniform corrosion on the mechanical properties of reinforcing bars. The present research provides a new approach for the establishment of corrosion damage constitutive models of reinforcing bars

Research and Evaluation of BDS Real-time Wide-area Precise Positioning Service System

By adopting the real-time observations of IGS, multi-GNSS experiment (MGEX) and national BDS augmentation service system (NBASS), BDS real-time wide-area precise positioning service system is developed, which can generate high accuracy BDS real-time orbits, clocks and ionosphere products and provide centimeter-, decimeter-and meter-level positioning service with different positioning modes including BDS dual-and single-frequency precise point positioning (PPP) and single point positioning (SPP). The evaluation results of real-time products show that the user range error (URE) of BDS real-time orbits and clocks is about 2.0 cm and the accuracy of real-time ionosphere products is better than 4.0 TECU. The performance of real-time kinematic positioning at the 95% confidence level is evaluated by real-time stations across China. It reveals that the BDS dual-frequency PPP shows significant regional characteristic, the accuracy in high-latitude and western fringe region is about 0.2 m and 0.3 m in the horizontal and vertical component, respectively, while the horizontal accuracy is better than 0.1 m and the vertical accuracy is better than 0.2 m in the midlands, which is close to the accuracy of GPS real-time PPP. By the combination of BDS and GPS, the BDS/GPS PPP can improve the positioning performance of GPS or BDS PPP, especially improves the PPP convergence time significantly and the convergence time is within 20 min. In addition, except the fringe region, BDS single-frequency PPP can achieve 0.5 m in horizontal component and 1.0 m in vertical component, BDS SPP can achieve 2.0 m and 3.0 m in horizontal and vertical components, respectively

Assessment of code bias variations of BDS triple-frequency signals and their impacts on ambiguity resolution for long baselines

Carrier phase ambiguity resolution over long baselines is challenging in BDS data processing. This is partially due to the variations of the hardware biases in BDS code signals and its dependence on elevation angles. We present an assessment of satellite-induced code bias variations in BDS triple-frequency signals and the ambiguity resolutions procedures involving both geometry-free and geometry-based models. First, since the elevation of a GEO satellite remains unchanged, we propose to model the single-differenced fractional cycle bias with widespread ground stations. Second, the effects of code bias variations induced by GEO, IGSO and MEO satellites on ambiguity resolution of extra-wide-lane, wide-lane and narrow-lane combinations are analyzed. Third, together with the IGSO and MEO code bias variations models, the effects of code bias variations on ambiguity resolution are examined using 30-day data collected over the baselines ranging from 500 to 2600 km in 2014. The results suggest that although the effect of code bias variations on the extra-wide-lane integer solution is almost ignorable due to its long wavelength, the wide-lane integer solutions are rather sensitive to the code bias variations. Wide-lane ambiguity resolution success rates are evidently improved when code bias variations are corrected. However, the improvement of narrow-lane ambiguity resolution is not obvious since it is based on geometry-based model and there is only an indirect impact on the narrow-lane ambiguity solutions

Generalized-positioning for mixed-frequency of mixed-GNSS and its preliminary applications

Modernized GPS and GLONASS, together with new GNSS systems, BeiDou and Galileo, offer code and phase ranging signals in three or more carriers. Traditionally, dual-frequency code and/or phase GPS measurements are linearly combined to eliminate effects of ionosphere delays in various positioning and analysis. This typical treatment method has imitations in processing signals at three or more frequencies from more than one system and can be hardly adapted itself to cope with the booming of various receivers with a broad variety of singles. In this contribution, a generalized-positioning model that the navigation system independent and the carrier number unrelated is promoted, which is suitable for both single- and multi-sites data processing. For the synchronization of different signals, uncalibrated signal delays (USD) are more generally defined to compensate the signal specific offsets in code and phase signals respectively. In addition, the ionospheric delays are included in the parameterization with an elaborate consideration. Based on the analysis of the algebraic structures, this generalized-positioning model is further refined with a set of proper constrains to regularize the datum deficiency of the observation equation system. With this new model, uncalibrated signal delays (USD) and ionospheric delays are derived for both GPS and BeiDou with a large dada set. Numerical results demonstrate that, with a limited number of stations, the uncalibrated code delays (UCD) are determinate to a precision of about 0.1 ns for GPS and 0.4 ns for BeiDou signals, while the uncalibrated phase delays (UPD) for L1 and L2 are generated with 37 stations evenly distributed in China for GPS with a consistency of about 0.3 cycle. Extra experiments concerning the performance of this novel model in point positioning with mixed-frequencies of mixed-constellations is analyzed, in which the USD parameters are fixed with our generated values. The results are evaluated in terms of both positioning accuracy and convergence time

Evaluation of strategies for the ultra-rapid orbit prediction of BDS GEO satellites

ABSTRACTThe quality of BeiDou Navigation Satellite System (BDS) Geostationary Earth Orbit (GEO) ultra-rapid products is unsatisfactory because GEO satellites are nearly stationary relative to ground stations. To optimize the quality of these ultra-rapid orbit products, we investigated the effects of the fitting arc length, an a priori Solar-Radiation Pressure (SRP) model, and the along-track empirical acceleration on the prediction of BDS GEO satellite orbits. The predicted orbit arcs of 24-h were evaluated through comparisons with the corresponding observed orbit arc and Satellite Laser Ranging (SLR) observations. In both eclipse and non-eclipse seasons, accuracy of the orbit predictions obtained using a 48-h fitting arc length were better than those obtained using 24-h and 72-h fitting arc lengths. Although the overlapping precision of predicted orbits exhibited no obvious improvement when an a priori SRP model was employed, the systematic bias in the SLR residuals was significantly reduced. Specifically, the mean value of SLR residuals decreased from −0.248 m to −0.024 m during non-eclipse seasons and from −0.333 m to −0.041 m during eclipse seasons, respectively. In addition, when an empirical acceleration in the along-track direction was introduced, the three-Dimensional Root-Mean-Square (3D RMS) of overlapping orbits during eclipse seasons decreased from 2.964 to 1.080 m, which is comparable to that during non-eclipse seasons. Furthermore, the Standard Deviation (STD) of SLR residuals decreased from 0.419 to 0.221 m during eclipse seasons. The analysis of SRP estimates shows that the stability of SRP parameters was significantly enhanced after the introduction of along-track empirical acceleration in eclipse seasons. The optimal BDS GEO ultra-rapid orbit prediction products were yielded by using a 48-h fitting arc length, an a priori SRP model and an along-track empirical acceleration

Impact of the Initial State on BDS Real-Time Orbit Determination Filter Convergence

High precision real-time orbit of navigation satellites are usually predicted based on batch estimation solutions, which is highly dependent on the accuracy of the dynamic model. However, for the BDS satellites, the accuracy and reliability of the predicted orbit usually decrease due to the inaccurate dynamic model or orbit maneuvers. To improve this situation, the sequential estimation Square Root Information Filtering (SRIF) was applied to determine the real-time BDS orbits. In the filter algorithm, usually a long period is required for the orbit to converge to the final accuracy, due to lake of accurate initial state. This paper focuses on the impact of the initial state with different a priori Standard Deviation (STD) on the BDS orbit convergence performance in both normal and abnormal periods. For the normal period, the Ultra-Rapid (UR) orbit products and the Broadcast Ephemerides (BRDC) used as initial orbits are discussed respectively. For the abnormal period, orbit maneuver is analyzed. Experimental results show that a proper a priori STD of initial state can significantly accelerate the orbit convergence, while a loose a priori STD takes more than 10 h to converge in the radial direction for the BDS GEO/IGSO/MEO satellites. When the UR orbit product is used as the initial orbit, the orbit of the IGSO/MEO satellites can converge to decimeter-level immediately. When the BRDC product is used, the accuracy of meter-level can be obtained for the IGSO/MEO immediately, and converge to decimeter-level in about 6 h. For the period after the orbit maneuver, the real-time orbit accuracy can reach meter-level in about 6 h after the first group of broadcast ephemerides is received