The multiple global navigation satellite systems (multi-GNSS) bring great opportunity for the real-time retrieval of high-quality zenith tropospheric delay (ZTD), which is a critical quality for atmospheric science and geodetic applications. In this contribution, a multi-GNSS precise point positioning (PPP) ambiguity resolution (AR) analysis approach is developed for real-time tropospheric delay retrieval. To validate the proposed multi-GNSS ZTD estimates, we collected and processed data from 30 Multi-GNSS Experiment (MGEX) stations; the resulting real-time tropospheric products are evaluated by using standard post-processed troposphere products and European Centre for Medium-Range Weather Forecasts analysis (ECMWF) data. An accuracy of 4.5 mm and 7.1 mm relative to the Center for Orbit Determination in Europe (CODE) and U.S. Naval Observatory (USNO) products is achievable for real-time tropospheric delays from multi-GNSS PPP ambiguity resolution after an initialization process of approximately 5 min. Compared to Global Positioning System (GPS) results, the accuracy of retrieved zenith tropospheric delay from multi-GNSS PPP-AR is improved by 16.7% and 31.7% with respect to USNO and CODE final products. The GNSS-derived ZTD time-series exhibits a great agreement with the ECMWF data for a long period of 30 days. The average root mean square (RMS) of the real-time zenith tropospheric delay retrieved from multi-GNSS PPP-AR is 12.5 mm with respect to ECMWF data while the accuracy of GPS-only results is 13.3 mm. Significant improvement is also achieved in terms of the initialization time of the multi-GNSS tropospheric delays, with an improvement of 50.7% compared to GPS-only fixed solutions. All these improvements demonstrate the promising prospects of the multi-GNSS PPP-AR method for time-critical meteorological applications.

Visual-Inertial extrinsic calibration is essential for achieving accurate and reliable data fusion in multi-source integrated navigation and positioning systems. However, most traditional methods rely on trajectory optimization driven by image features and inertial pre-integration, which are highly susceptible to noise, fitting errors, and other interfering factors. To address this limitation, we propose a novel visual-inertial calibration framework. Specifically, the framework employs an efficient Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) postprocessing method to generate high-precision carrier trajectories, thereby replacing the poses derived from inertial navigation. Meanwhile, it computes the spatial coordinates of landmarks in the Earth-Centered Earth-Fixed (ECEF) coordinate system and reconstructs the visual projection equation. A unified global least-squares optimization problem is formulated to obtain the globally optimal extrinsic parameters. The Monte Carlo simulations analyze the impact of reference poses on calibration accuracy, demonstrating that our proposed algorithm achieves at least 54.1% and 54.6% improvements in attitude and translation accuracy, respectively, compared with the Kalibr toolbox. Additionally, three real-world experiments evaluate the positioning accuracy of visual-inertial odometry (VIO) to inversely verify the calibration performance. The results demonstrate that, across all datasets, the VIO system calibrated with the proposed extrinsic parameters achieves significant position accuracy improvements of 45.3%, 14.3%, and 30.3%, respectively. This further validates the feasibility and superiority of the GNSS/INS post-processing-assisted extrinsic parameter calibration method.

Accurate and reliable reference poses are a crucial foundation for numerous scientific and engineering applications. This study introduces a two-stage reference pose generation method to produce a more reliable trajectory for large-scale outdoor environments. The first stage is a tightly coupled GNSS/INS integration to generate initial poses, which consists of a forward Extended Kalman filter (EKF) and a backward Rauch–Tung–Streibel smoother (RTSS). In the second stage, raw IMU measurements, line/plane features extracted from LiDAR point clouds, and GNSS RTK solutions, including position and velocity, are fed to an optimization procedure based on continuous-time pose representation to refine the initial poses yielded in the first stage. Two vehicle experiments are conducted to validate the effectiveness of our proposed algorithm. The results demonstrate that our proposed method can significantly reduce position errors in both GNSS partly blocked and challenging environments, with improvements (71.4%, 72.4%, 69.8%) and (67.3%, 68.8%, 63.6%) in the east, north, and up directions in comparison with traditional tightly coupled GNSS/INS integration. Additionally, enhancements in attitude estimation are observed, particularly for the yaw angle.