Global positioning system (GPS) has been widely used to estimate the absolute position of outdoor vehicles. Global positioning system and inertial measurement unit (IMU) can be integrated to deliver an enhanced effect over the individual system. GPS/IMU navigation solution that utilizes time-differenced carrier phase (TDCP) as observation can achieve high-precision positioning. The traditional TDCP-GPS/IMU navigation methods are compromised by the loss of constraints, especially when cycle slip occurs. This article proposes an optimal time difference-based TDCP-GPS/IMU navigation using graph optimization. Different from current methods, an optimal time difference strategy is designed to formulate integer-free constraints that span multiple epochs in a time window. Each satellite can be fully used to restrain the accumulation errors even though cycle slip happens. Then, the integer-free constraints are fused with relative constraints from IMU preintegration and TDCP between two successive epochs using graph optimization. Field tests are carried out to validate the performance of the proposed method. The results show that the proposed method can make the most of window length constraints to improve the positioning performance compared with traditional methods.

Multi-sensor integrated navigation systems based on factor graph are increasingly used on indoor robots, UAVs, and other vehicles. The output information of the equipped low-cost inertial measurement unit (IMU) is usually processed by IMU pre-integration techniques. As the accuracy of IMU increases, the traditional factor graph using the IMU pre-integration method need to be improved. This paper proposes a factor graph optimization algorithm for high-precision IMU based navigation system. An improved IMU pre-integration method is used in the algorithm to deal with the data from inertial sensors. Different from traditional methods, the effect of the curvature of the Earth’s surface on the IMU pre-integration method is taken into account. Meanwhile, the parameters affecting the accuracy of the IMU pre-integration method are corrected by the estimated navigation state of the carrier. Thus, a more accurate relative constraint is constructed. After that, this constraint and other measurement information are fused by the factor graph optimization algorithm. Finally, different simulation tests and field vehicle tests are carried out to validate the performance of the proposed method. The test results show that the proposed method can improve the carrier positioning accuracy by 20% to 90% when using high-precision inertial sensors under different conditions.

In this paper, a new fault diagnosis method is presented for the sensors in the vertical direction of a quadrotor. Different from the existing methods which treat the inertial sensors and the measurement sensors separately, the presented method is capable of dealing with both the z-axis accelerometer and the barometer. The knowledge of the thrust model is used to generate an analytical redundancy based fault diagnosis approach for altitude estimation. The filter design, fault detection, isolation and recovery problems are addressed. An improved chi-test method is used for fault detection. Real flight data is used to validate the proposed approaches, showing that 1) the faults of the z-axis accelerometer and the barometer can both be detected; 2) the thrust model of a quadrotor can be used to replace the faulty z-axis accelerometer.

The presence of asynchronous absolute and relative measurements has posed a great challenge to the current multisensor positioning method in robotic systems. Although traditional factor graph methods possess a capability of plug-and-play, only a compromised performance can be obtained when dealing with an increasing number of asynchronous observations. In this article, a novel plug-and-play factor graph method for asynchronous absolute/relative measurements fusion in multisensor positioning is proposed. Different from traditional methods, a fixed-rate graph model is formed. The variable nodes in the graph are built at a fixed update rate to do the optimization, which is not affected by the arrival of measurements. Asynchronous absolute and relative measurements between two successive variable nodes are associated with corresponding variable nodes in the graph via the propagation of closed-form inertial measurement unit (IMU) preintegration. The simulations, datasets, and field tests are carried out to validate the proposed method. The results indicate that the closed-form IMU preintegration method has better dynamic adaptability in fusion system to formulate the association with asynchronous measurements. On this basis, the proposed method can integrate asynchronous measurements in a plug-and-play manner and achieve better performance compared to current methods. Meanwhile, the computational load can also be reduced.

The integration of global navigation satellite system (GNSS), inertial measurement unit (IMU) and odometer has been widely utilized in vehicular localization. Due to the advantage in accessing accurate state estimation, factor graph is commonly employed to achieve the integration of vehicular sensors. However, the fusion performance of traditional factor graph reduces when dealing with asynchronous and abnormal measurements, yielding compromised estimation results for localization and calibration. In this paper, an enhanced adaptable factor graph for simultaneous localization and calibration (SLAC) in GNSS/IMU/odometer integration is proposed. First, a novel factor graph framework for GNSS/IMU/odometer integration is designed. In view of the asynchronism among sensors, the variable nodes in factor graph are built according to odometer measurement. IMU pre-integration is then used to establish the relationship between asynchronous GNSS position and corresponding variable nodes to form residuals. To properly deal with the coupling noise in the proposed model, the expectation maximum (EM) algorithm is utilized to estimate the noise parameters based on the proposed model. Simulations and field tests are done to validate the proposed method. It shows that the proposed method has better estimation accuracy for localization and calibration compared with traditional methods. Meanwhile, the computational load of the proposed method is discussed.

In a dual-axis rotational inertial navigation system (RINS), there are two kinds of installation errors, nonorthogonal installation errors of inertial sensors, and installation errors between the inertial measurement unit (IMU) and rotation axes. Traditionally, these two errors are not considered simultaneously. Thus, they are calibrated separately by different estimation algorithms and rotation schemes. In this paper, a system-level self-calibration method for installation errors of a dual-axis RINS is proposed. Based on the Kalman filter, the measurement model is reestablished to ensure that all installation errors can be estimated together. First, the relationship between the initial attitude and subsequent attitude of IMU during rotation is used as a constraint to estimate nonorthogonal installation errors of accelerometers, and installation errors between the IMU and rotation axes. Then, the angular rate of the rotation mechanism is used as a reference to estimate nonorthogonal installation errors of the gyros. The rotation scheme of the IMU is designed to make all installation errors observable, and the observability of the system is analyzed based on the piecewise constant system method. Simulation and laboratory experiment results suggest that installation errors can be effectively estimated by the proposed method, thereby avoiding the complex separating process.