3D LiDAR Aided GNSS/INS Integration Fault Detection, Localization and Integrity Assessment in Urban Canyons

Abstract

The performance of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integrated navigation can be severely degraded in urban canyons due to the non-line-of-sight (NLOS) signals and multipath effects. Therefore, to achieve a high-precision and robust integrated system, real-time fault detection and localization algorithms are needed to ensure integrity. Currently, the residual chi-square test is used for fault detection in the positioning domain, but it has poor sensitivity when faults disappear. Three-dimensional (3D) light detection and ranging (LiDAR) has good positioning performance in complex environments. First, a LiDAR aided real-time fault detection algorithm is proposed. A test statistic is constructed by the mean deviation of the matched targets, and a dynamic threshold is constructed by a sliding window. Second, to solve the problem that measurement noise is estimated by prior modeling with a certain error, a LiDAR aided real-time measurement noise estimation based on adaptive filter localization algorithm is proposed according to the position deviations of matched targets. Finally, the integrity of the integrated system is assessed. The error bound of integrated positioning is innovatively verified with real test data. We conduct two experiments with a vehicle going through a viaduct and a floor hole, which, represent mid and deep urban canyons, respectively. The experimental results show that in terms of fault detection, the fault could be detected in mid urban canyons and the response time of fault disappearance is reduced by 70.24% in deep urban canyons. Thus, the poor sensitivity of the residual chi-square test for fault disappearance is improved. In terms of localization, the proposed algorithm is compared with the optimal fading factor adaptive filter (OFFAF) and the extended Kalman filter (EKF). The proposed algorithm is the most effective, and the Root Mean Square Error (RMSE) in the east and north is reduced by 12.98% and 35.1% in deep urban canyons. Regarding integrity assessment, the error bound can overbound the positioning errors in deep urban canyons relative to the EKF and the mean value of the error bounds is reduced.

Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments

“…Then, a probabilistic factor graph was used to combine the data from the inertial sensor module (employing a gyroscope, a magnetometer, and an accelerometer) that provides a heading hint and the localization data from a GNSS receiver. Wang et al [75] used the data obtained from a LiDAR laser scanner in order to correct the position localization faults generated by a GNSS localization system and a subsystem consisting of IMU sensors. Xiong et al [76] proposed a modular localization system that uses image data, data collected from IMU sensors, information about the motion of the vehicle, and data from a GNSS receiver.…”

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

“…The residual chi-square statistical method is typically applied in navigation fault detection system, and the detection function is constructed to calculate the index through Kalman filter (KF) method, which detects the positioning faults of sensors based on the prediction information and determines whether a fault exists [24]. Wang et al constructed the IMU/GNSS tightly coupled system based on EKF and detected the system faults with the assistance of LiDAR real-time estimation [25]. In urban areas, GNSS signals may suffer from the multipath effect or non-line-of-sight (NLOS) signal interference due to reflections, and the whole system tends to provide poor positioning results.…”

Two-Level Integrity-Monitoring Method for Multi-Source Information Fusion Navigation

“…If these faults are not promptly detected, the entire navigation system may be compromised by erroneous data, resulting in a reduction in navigation accuracy. In severe cases, such failures can lead to overall breakdown of the navigation system [1,2]. Therefore, establishing an integrity-monitoring method for a multi-source information fusion navigation system is a crucial issue that requires resolution.…”