This article proposes an improved global navigation satellite system (GNSS) positioning method that explores the time correlation between consecutive epochs of the code and carrier-phase measurements, which significantly increases the robustness against outlier measurements. Instead of relying on the time difference carrier phase which only considers two neighboring epochs using an extended Kalman filter estimator, this article proposed to employ the carrier-phase measurements inside a window, the so-called window carrier phase (WCP), to constrain the states inside a factor graph. A left null space matrix is employed to eliminate the shared unknown ambiguity variables and, therefore, correlate the associated states inside the WCP. Then, the pseudorange, Doppler, and the constructed WCP measurements are integrated simultaneously using factor graph optimization to estimate the state of the GNSS receiver. We evaluated the performance of the proposed method in two typical urban canyons in Hong Kong, achieving the mean positioning errors of 1.76 and 2.96 m, respectively, using the automobile-level GNSS receiver. Meanwhile, the effectiveness of the proposed method is further evaluated using a low-cost smartphone-level GNSS receiver, and similar improvement is also obtained when compared with several existing GNSS positioning methods.

Robust and globally‐referenced positioning is indispensable for autonomous driving vehicles. Global navigation satellite system (GNSS) is still an irreplaceable sensor. Satisfactory accuracy (about 1 m) can be obtained in sparse areas. However, the GNSS positioning error can be up to 100 m in dense urban areas due to the multipath effects and non‐line‐of‐sight (NLOS) receptions caused by reflection and blockage from buildings. NLOS is currently the dominant factor degrading the performance of GNSS positioning. Recently, the camera has been employed to detect the NLOS and then to exclude the NLOS measurements from GNSS calculation. The exclusion of NLOS measurements can cause severe distortion of satellite distribution, due to the excessive NLOS receptions in deep urban canyons. Correcting the NLOS receptions with the aid of 3D light detection and ranging after detection of NLOS receptions using a fish‐eye camera was proposed in this study. Finally, the GNSS positioning was improved by using the healthy and corrected NLOS pseudo‐range measurements. The proposed method is evaluated through real road tests in typical highly urbanised canyons of Hong Kong. The evaluation results show that the proposed method can effectively improve the positioning performance.

The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature-tracking process which is critical to the feature-based VINS. One well-known method that mitigates the effects of dynamic objects is to detect vehicles using deep neural networks and remove the features belonging to surrounding vehicles. However, excessive feature exclusion can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this study proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of the VINS. First, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurement by integrating two parts: (1) the geometry of feature distribution (GFD); (2) the quality of feature tracking. Second, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the effects of outlier measurements, like the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance on the excessive parameterization of the M-estimator. Experiments were conducted in typical urban areas of Hong Kong with numerous dynamic objects. The results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of the VINS is obtained when compared with the conventional VINS method.

In this article, we proposed a graduated nonconvexity (GNC) aided outlier mitigation method for the improvement of the visual-inertial integrated navigation system (VINS) to face the challenge of dynamic environments with numerous unexpected outlier measurements. A GNC optical flow algorithm was proposed for the detection of the outliers of feature tracking in the front-end of VINS by iteratively estimating the optical flow and the optimal weightings of feature correspondences. Then the feature correspondences with small weightings were excluded. However, excessive outlier exclusion may cause insufficient constraints on the state, causing degeneration of VINS. To solve the problem, this article proposed to detect the potential degeneration based on the degree of constraint in different directions of the pose estimation. Then the number of features being considered was intelligently adapted based on the degeneration level to improve the geometry constraint in the coming epochs. We evaluated the effectiveness of the proposed method by using two challenging datasets (including challenging night scenarios) collected in urban canyons of Hong Kong. The results show that the proposed method can effectively reject the potential outlier visual measurements, and alleviate the degeneration, leading to improved positioning performance in both evaluated datasets.

The Global Navigation Satellite System (GNSS) is one of the most popular solutions to localize potential road cracks. Unfortunately, the accurate positioning of the GNSS in urban environments presents a significant challenge due to complex signal blockage and reflection phenomena. To tackle this, we propose a method that enhances GNSS positioning accuracy, particularly suited for the intricate layouts of urban canyons. Our approach integrates the prior map with lane line information into the Factor Graph Optimization (FGO) algorithm, effectively mitigating the impacts of multi-path effects and NLOS. However, the poor accuracy of the initial guess from the GNSS positioning can easily lead to incorrect lane matching which is one of the main challenges of lane matching in highly urbanized areas. To fill this gap, this paper proposes to use the switchable factor to model the potential incorrect lane matching by leveraging the redundancy of lane information across multi-epochs. This paper verified the effectiveness of this methodology using two datasets from the dense urban environment of Hong Kong, collected using the low-cost automobile level receiver, and compared the results with conventional methods. Our findings affirm that integrating the FGO-based GNSS positioning system with map information significantly boosts positioning accuracy, demonstrating the robustness of our approach.

Accurate and globally referenced positioning is crucial to autonomous systems with navigation requirements, such as unmanned aerial vehicles (UAV) and autonomous driving vehicles (ADV). GNSS/LiDAR integration is a popular sensor pair that can provide outstanding positioning performance in open areas. However, the accuracy is significantly degraded in urban canyons, due to the excessive unmodeled non-Gaussian GNSS outliers caused by multipath effects and none-line-of-sight (NLOS) receptions. As a result, the violation of the Gaussian assumption can severely distort the sensor fusion process, such as the extended Kalman filter (EKF). To mitigate the effects of these non-Gaussian GNSS outliers, this paper proposes to leverage the Gaussian mixture model (GMM) to describe the potential noise of GNSS positioning and apply it to further sensor fusion. Instead of relying on excessive offline parameterization and tuning, the parameters of the GMM are estimated simultaneously based on the residuals of the GNSS measurements using an expectation-maximization (EM) algorithm. Then the state-of-the-art factor graph optimization (FGO) is applied to integrate the GNSS positioning and LiDAR odometry based on the estimated GMM. The experiment in a typical urban canyon is conducted to validate the performance of the proposed method. The result shows that the GMM can effectively mitigate the effects of GNSS outliers and improves positioning performance.