High-accuracy localization systems for non-line-of-sight (NLOS) environments have been a popular focus of research on indoor positioning. In particular, the use of ultra-wideband (UWB) technology for ranging is emerging as an effective method of indoor localization due to its high accuracy. To improve the overall performance of UWB systems, combining UWB technology with an inertial navigation system (INS) is a commonly used indoor positioning approach. However, UWB measurements are affected by outliers under the NLOS propagation of radio signals. In addition, INS tend to suffer from error accumulation without constraints imposed by observations from other sensors. To address these problems, an adaptive robust extended Kalman filter algorithm based on tightly coupled UWB/INS/floor map integration is proposed, with which the noise in the UWB observations can be adaptively adjusted. The integrated system performs NLOS identification based on a map line segment matching algorithm; then, abnormal observations are abandoned, or their weights are reduced to prevent the error accumulation of the INS from being incorrectly constrained. Finally, an evaluation based on experimental data shows the significant enhancement achieved with the proposed algorithm based on the integration of UWB technology, an INS and a floor map, which can achieve a 2-D positioning accuracy that corresponds to a root mean square error of 0.27 m. Thus, substantial improvements can be achieved for long-term operation in NLOS environments using the proposed integration scheme.

Craters on the lunar surface are the most direct method for the study of geological processes and are of great significance to the study of lunar evolution. In order to fill the research gap on small craters (diameter less than 3 m), we focus on the small craters around the moving path of the Yutu-2 lunar rover and carry out a 3D reconstruction and geometrical morphology analysis on them. First, a self-calibration model with multiple feature constraints is used to calibrate the navigation camera and obtain the internal and external parameters. Then, the sequence images with overlapping regions from neighboring stations are used to obtain the precise position of the rover through the bundle adjustment (BA) method. After that, a cross-scale cost aggregation for a stereo matching network is proposed to obtain a parallax map, which can further obtain 3D point clouds of the lunar surface. Finally, the indexes of the craters are extracted (diameter D, depth d, and depth–diameter ratio d_r), and the different indicators are fitted and analyzed. The results suggest that CscaNet has an anomaly percentage value of 1.73% in the KITTI2015 dataset, and an EPE of 0.74 px in the SceneFlow dataset, both of which are superior to GC-Net, DispNet, and PSMnet, and have higher reconstruction accuracy. The correlation between D and d is high and exhibits a positive correlation, while the correlation between D and d_r is low. The geometric morphology expressions of small craters fitted by using D and d are significantly different from the expressions proposed by other scholars for large craters. This study provides a priori knowledge for the subsequent Von Karmen crater survey mission in the SPA Basin.

Reliable absolute positioning is indispensable in long-term positioning systems. Although simultaneous localization and mapping based on light detection and ranging (LiDAR-SLAM) is effective in global navigation satellite system (GNSS)-denied environments, it can provide only local positioning results, with error divergence over distance. Ultrawideband (UWB) technology is an effective alternative; however, non-line-of-sight (NLOS) propagation in complex indoor environments severely affects the precision of UWB positioning, and LiDAR-SLAM typically provides more robust results under such conditions. For robust and high-precision positioning, we propose an improved-UWB/LiDAR-SLAM tightly coupled (TC) integrated algorithm. This method is the first to combine a LiDAR point cloud map generated via LiDAR-SLAM with position information from UWB anchors to distinguish between line-of-sight (LOS) and NLOS measurements through obstacle detection and NLOS identification (NI) in real time. Additionally, to alleviate positioning error accumulation in long-term SLAM, an improved-UWB/LiDAR-SLAM TC positioning model is constructed using UWB LOS measurements and LiDAR-SLAM positioning information. Parameter solving using a robust extended Kalman filter (REKF) to suppress the effect of UWB gross errors improves the robustness and positioning performance of the integrated system. Experimental results show that the proposed NI method using the LiDAR point cloud can efficiently and accurately identify UWB NLOS errors to improve the performance of UWB ranging and positioning in real scenarios. The TC integrated method combining NI and REKF achieves better positioning effectiveness and robustness than other comparative methods and satisfactory control of sensor errors with a root-mean-square error of 0.094 m, realizing subdecimeter indoor positioning.

In the field of surveying and mapping, inertial sensor deterministic errors are poorly understood, and error calibration and compensation are not carried out. Thus, in this study, the effects of three types of deterministic errors (i.e., bias, scale factor error, and installation error) in a conventional inertial measurement unit (IMU) error model on a navigation system are theoretically deduced and verified by simulation. Subsequently, navigation experiments are carried out to investigate the effects of the three deterministic errors on the navigation system. The experimental results show that the gyro bias has the strongest influence on the navigation and positioning accuracy of the system. Consequently, we designed a two-position continuous calibration scheme to calibrate the IMU. The calibration scheme can simultaneously calibrate the bias error of the gyroscope and the accelerometer. When calibrating the bias error of the 0.005°/h order of magnitude, the maximum relative error is 13.16%, and the rest of the calibration relative errors are less than 10%, which verifies the effectiveness of the calibration path designed in this paper. The system is compensated by using the IMU bias calibration results, and the navigation experiment results show that the position accuracy and heading accuracy are improved by 72.68% and 79.65%, respectively, through the calibration and compensation of IMU bias error. Therefore, the position and heading accuracy of the system will be greatly improved by calibrating and compensating the bias error through the two-position calibration path before the IMU is used.

Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects.

As the largest groundwater drainage region in China, the per capita water resources in the North China Plain (NCP) account for only one-seventh of the country’s available water resources. Currently, the NCP is experiencing a serious water shortage due to the overexploitation of groundwater resources and a subsequent series of natural disasters. Thus, accurate regional assessments and effective water resource management policies are of critical importance. To accomplish this phenomenon, the daily terrestrial water storage anomaly (TWSA) over the NCP is calculated from the combination of the GNSS vertical deformation sequences (seasonal items) and GRACE (trend items). The groundwater storage anomaly (GWSA) in the NCP is obtained by subtracting the canopy water, soil water, and snow water equivalent components from the TWSA. The inversion results of this study are verified by comparisons with the Global Land Data Assimilation System (GLDAS) data products. The elevated annual amplitude areas are located in Beijing and Tianjin, and the Pearson correlation coefficient (PCC), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) between the two GWSA results are 0.67, 4.01 cm, and 0.61, respectively. This indicates that the methods proposed in this study are reliable. Finally, the groundwater drought index was calculated for the period from 2011 to 2021, and the results showed that 2019 was the driest year, with a drought severity index value of −0.12, indicative of slightly moderate drought conditions. By calculating and analyzing the annual GWSA, this work shows that the South–North Water Transfer Project does provide some regional drought mitigation.