Visual navigation systems are susceptible to complex environments, while inertial navigation systems (INS) are not affected by external factors. Hence, we present IC-GVINS, a robust, real-time, INS-centric global navigation satellite system (GNSS)-visual-inertial navigation system to fully utilize the INS advantages. The Earth rotation has been compensated in the INS to improve the accuracy of high-grade inertial measurement units (IMUs). To promote the system robustness in high-dynamic conditions, the precise INS information is employed to assist the feature tracking and landmark triangulation. With a GNSS-aided initialization, the IMU, visual, and GNSS measurements are tightly fused in a unified world frame within the factor graph optimization framework. Dedicated experiments were conducted in the public vehicle and private robot datasets to evaluate the proposed method. The results demonstrate that IC-GVINS exhibits superior robustness and accuracy in complex environments. The proposed method with the INS-centric architecture yields improved robustness and accuracy compared to the state-of-the-art methods.

In filtering-based global navigation satellite system (GNSS) / inertial navigation system (INS) integrated navigation systems, the precise INS mechanization has fully considered the impact of the Earth rotation, Coriolis acceleration, etc. However, most inertial measurement unit (IMU) preintegration models in factor graph optimization (FGO)-based integrated navigation systems are rough and ignore these factors. We propose an FGO-based GNSS/INS integrated navigation system to analyze and evaluate the impact of the Earth rotation on MEMS-IMU preintegration. The proposed FGO-based integration is based on a refined IMU preintegration, in which the Earth rotation is compensated. The proposed GNSS/INS integration is a sliding-window optimizer in which the GNSS positioning and the IMU preintegration are tightly fused within the FGO framework. The simulated GNSS outage in field tests, an effective method, is adopted to evaluate the IMU preintegration quantitatively. The results demonstrate that the refined IMU preintegration can achieve the same accuracy as the precise INS mechanization. In contrast, the rough IMU preintegration and INS mechanization without compensating for the Earth rotation yields notable accuracy degradation. When the GNSS-outage time is 60 seconds, the degradation can be 200% for the industrial-grade MEMS module and more than 10% for the consumer-grade MEMS chip. Besides, the degradation can be more significant if the GNSS-outage time is longer. The proposed FGO-based GNSS/INS integration ( https://github.com/i2Nav-WHU/OB_GINS ) and the employed datasets ( https://github.com/i2Nav-WHU/awesome-gins-datasets ) are open-sourced on GitHub.

Accurate measurement of the railway track geometry is a task of fundamental importance to ensure the track quality in both the construction phase and the regular maintenance stage. Conventional track geometry measuring trolleys (TGMTs) in combination with classical geodetic surveying apparatus such as total stations alone cannot meet the requirements of measurement accuracy and surveying efficiency at the same time. Accurate and fast track geometry surveying applications call for an innovative surveying method that can measure all or most of the track geometric parameters in short time without interrupting the railway traffic. We provide a novel solution to this problem by integrating an inertial navigation system (INS) with a geodetic surveying apparatus, and design a modular TGMT system based on aided INS, which can be configured according to different surveying tasks including precise adjustment of slab track, providing tamping measurements, measuring track deformation and irregularities, and determination of the track axis. TGMT based on aided INS can operate in mobile surveying mode to significantly improve the surveying efficiency. Key points in the design of the TGMT’s architecture and the data processing concept and workflow are introduced in details, which should benefit subsequent research and provide a reference for the implementation of this kind of TGMT. The surveying performance of proposed TGMT with different configurations is assessed in the track geometry surveying experiments and actual projects.

Using a microelectromechanical system (MEMS) inertial measurement unit (IMU) array composed of multiple low-cost IMUs can reduce the measurement errors of inertial sensors, and improve its navigation performance. However, there is doubt about the benefit of the IMU array in the scene of GNSS/INS dynamic navigation . Therefore, to evaluate the navigation performance of the arrays, we developed four groups of IMU arrays, each containing 16 MEMS IMUs. Each IMU was accurately calibrated and compensated to improve the performance. The field experiments proved that the navigation accuracy of the IMU array improved by 3.4 times statistically over a single IMU, which is close to the theoretical limit, i.e., 4 times. Comparison data processing indicated that the individual IMU calibration reduced the horizontal position error of the array by 54% on average, which confirms that the precise calibration of each IMU, especially the cross-axis effect and mounting angles, is crucial to the array’s navigation performance. This research provides firm experimental support for the application of IMU arrays in the field of navigation.

Odometer has been proven to significantly improve the robustness and accuracy of the Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated vehicle navigation in GNSS-denied environments. However, odometer is inaccessible in many applications, especially for aftermarket devices and smartphones. To apply forward speed aiding without hardware wheeled odometer, we propose OdoNet, an untethered one-dimensional Convolution Neural Network (CNN)-based pseudo-odometer model learning from a single Inertial Measurement Unit (IMU). Dedicated experiments have been conducted to verify the generalization capability and the precision of the OdoNet. The results indicate that the IMU individuality, the vehicle loads, and the road conditions have little impact on the robustness and precision of the OdoNet, while the IMU biases and the mounting angles may notably ruin the OdoNet. Hence, a data-cleaning procedure is adopted to effectively mitigate the impacts of the IMU biases and the mounting angles. Compared to the processing mode using only non-holonomic constraint (NHC), by employing the pseudo-odometer, the positioning error is reduced by around 68%, while the percentage is around 74% for the hardware wheeled odometer. In conclusion, the proposed OdoNet can be employed as an untethered pseudo-odometer for vehicle navigation.

Accurate and long-distance depth estimation for visual landmarks is challenging in visual-inertial navigation systems (VINS). In visual-degenerated scenes with illumination changes, moving objects, or weak texture, depth estimation may be more difficult, resulting in poor robustness and accuracy. For low-speed robot navigation, we present a solid-state-LiDAR-enhanced VINS (LE-VINS) to improve the system robustness and accuracy in challenging environments. The point clouds from the solid-state LiDAR are projected to the visual keyframe with the inertial navigation system (INS) pose for depth association while compensating for the motion distortion. A robust depth-association method with an effective plane-checking algorithm is proposed to estimate the landmark depth. With the estimated depth, we present a LiDAR depth factor to construct accurate depth measurements for visual landmarks in factor graph optimization (FGO). The visual feature, LiDAR depth, and IMU measurements are tightly fused within the FGO framework to achieve maximum-a-posterior estimation. Field tests were conducted on a low-speed robot in large-scale challenging environments. The results demonstrate that the proposed LE-VINS yields significantly improved robustness and accuracy compared to the original VINS. Besides, LE-VINS exhibits superior accuracy than the state-of-the-art LiDAR-visual-inertial navigation system. LE-VINS also outperforms the existing LiDAR-enhanced method, benefiting from the robust depth-association algorithm and the effective LiDAR depth factor.