Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.

In smoggy and dusty environments, vision- and laser-based localization methods are not able to be used effectively for controlling the movement of a robot. Autonomous operation of a security robot can be achieved in such environments by using millimeter wave (MMW) radar for the localization system. In this study, an approximate center method under a sparse point cloud is proposed, and a security robot localization system based on millimeter wave radar is constructed. To improve the localization accuracy of the robot, inertial localization of the robot is integrated with MMW radar. Based on the concept of inertial localization, the state equation for the motion principle of the robot is deduced. According to principle of MMW localization, the measurement equation is derived, and a kinematics model of the robot is constructed. Further, by applying the Kalman filtering algorithm, a fusion localization system of the robot based on MMWs and inertial localization is proposed. The experimental results show that with iterations of the filtering algorithm, the gain matrix converges gradually, and the error of the fusion localization system decreases, leading to the stable operation of the robot. Compared to the localization system with only MMW radar, the average localization error is approximately reduced from 11 cm to 8 cm, indicating that the fusion localization system has better localization accuracy.

In smog and dust environments, vision and laser-based navigation methods can not be used effectively for controlling the movement of a robot. Autonomous operation of a security robot can be achieved in such environments by using millimeter wave (MMW) radar for the navigation system. In this study, an approximate center method under sparse point cloud is proposed, and a security robot navigation system based on millimeter wave radar is constructed. To improve the navigation accuracy of the robot, inertial navigation of the robot is integrated with MMW radar. Based on the concept of inertial navigation, the state equation for the motion principle of the robot is deduced. According to principle of MMW navigation, the measurement equation is derived, and the kinematics model of the robot is constructed. Further, by applying the Kalman filtering algorithm, a fusion navigation system of the robot based on MMW and inertial navigation is proposed. The experimental results show that the navigation error is close to the error of the navigation system with only MMW in the initial stage. With iterations of the filtering algorithm, the gain matrix converges gradually, and the error of the fusion navigation system decreases, leading to the stable operation of the robot. The localization error of the fusion navigation system is approximately 0.08 metre, and the navigation accuracy is better than that of the navigation system with only MMW radar.