Accurate navigation is essential for autonomous robots and vehicles. In recent years, the integration of the Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and camera has garnered considerable attention due to its robustness and high accuracy in diverse environments. However, leveraging the full capacity of GNSS is cumbersome because of the diverse choices of formulations, error models, satellite constellations, signal frequencies, and service types, which lead to different precision, robustness, and usage dependencies. To clarify the capacity of GNSS algorithms and accelerate the development efficiency of employing GNSS in multi-sensor fusion algorithms, we open source the GNSS/INS/Camera Integration Library (GICI-LIB), together with detailed documentation and a comprehensive land vehicle dataset. A factor graph optimization-based multi-sensor fusion framework is established, which combines almost all GNSS measurement error sources by fully considering temporal and spatial correlations between measurements. The graph structure is designed for flexibility, making it easy to form any kind of integration algorithm. For illustration, RTK, PPP, and four Real-Time Kinematic (RTK)-based algorithms from GICI-LIB are evaluated using our dataset and public datasets. Results confirm the potential of the GICI system to provide continuous precise navigation solutions in a wide spectrum of urban environments.

This letter proposes an extrinsic calibration approach for a pair of monocular camera and prism-spinning solid-state LiDAR. The unique characteristics of the point cloud measured resulting from the flower-like scanning pattern is first disclosed as the vacant points, a type of outlier between foreground target and background objects. Unlike existing method using only depth continuous measurements, we use depth discontinuous measurements to retain more valid features and efficiently remove vacant points. The larger number of detected 3D corners thus contain more robust a priori information than usual which, together with the 2D corners detected by overlapping cameras and constrained by the proposed circularity and rectangularity rules, produce accurate extrinsic estimates. The algorithm is evaluated with real field experiments adopting both qualitative and quantitative performance criteria, and found to be superior to existing algorithms. The code is available on GitHub.

Reliable navigation in complex urban environments is challenging due to frequent GNSS signal outages and multipath interference. A practical mitigation is to incorporate LiDAR-inertial odometry (LIO) to bound drift during GNSS gaps and maintain local accuracy. However, commonly adopted scan-to-map constraints may yield inconsistencies when global corrections conflict with a static map in a tightly coupled framework. To address these issues, this letter presents GLINS, a measurement-level tightly coupled GNSS-LiDAR-INS system that features a consistent estimator design with explicit landmark states. GLINS extracts stable landmarks from the LiDAR voxel map and treats them as explicit variables in the state estimator, allowing the map representation to co-evolve with global corrections. The proposed estimator is implemented as a keyframe-based sliding-window factor graph that tightly fuses raw GNSS pseudorange, carrier-phase, and Doppler measurements with IMU preintegration and LiDAR landmark factors. A robust GNSS module performs fault detection and exclusion and carrier-phase ambiguity resolution, further improving reliability. Experiments on a public benchmark and proprietary urban driving sequences demonstrate that GLINS achieves improved estimator consistency compared to representative baselines and delivers high-precision, drift-free trajectories across diverse urban scenarios.

Accurate navigation is essential for autonomous robots and vehicles. In recent years, the integration of the Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and camera has garnered considerable attention due to its robustness and high accuracy in diverse environments. However, leveraging the full capacity of GNSS is cumbersome because of the diverse choices of formulations, error models, satellite constellations, signal frequencies, and service types, which lead to different precision, robustness, and usage dependencies. To clarify the capacity of GNSS algorithms and accelerate the development efficiency of employing GNSS in multi-sensor fusion algorithms, we open source the GNSS/INS/Camera Integration Library (GICI-LIB), together with detailed documentation and a comprehensive land vehicle dataset. A factor graph optimization-based multi-sensor fusion framework is established, which combines almost all GNSS measurement error sources by fully considering temporal and spatial correlations between measurements. The graph structure is designed for flexibility, making it easy to form any kind of integration algorithm. For illustration, Real-Time Kinematic (RTK), Precise Point Positioning (PPP), and four RTK-based algorithms from GICI-LIB are evaluated using our dataset and public datasets. Results confirm the potential of the GICI system to provide continuous precise navigation solutions in a wide spectrum of urban environments.