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In previous navigation positioning methods, a micro inertial measurement unit (MIMU) provides navigation information by measuring the linear acceleration and angular velocity of the quadrotors. However, low-cost MIMU often fails in harsh environments and produces large misalignment angles during initial alignment, reducing the navigation performance of quadrotors. Therefore, this article introduces the dynamics model of quadrotors, integrating the roll and pitch moment model, torque model, drag model, thrust model, XYZ-axis gyroscope, XYZ-axis accelerometer, and global navigation satellite system (GNSS) to form an MIMU fault-tolerant federated Kalman filter based on analytical redundancy. Meanwhile, this article introduces the Lie group manifold theory into the unscented Kalman filter (UKF) to improve the attitude estimation accuracy under large misalignment angles and reduce the influence of attitude errors on velocity and position. The proposed federated UKF on the Lie group manifold can accurately estimate navigation parameters when MIMU fails. Real flight experiment data were used for verification, showing that faults in the XYZ-axis gyroscope, XYZ-axis accelerometer, and dynamics model of quadrotors can be detected and isolated. When there are no faults, the attitude, velocity, and position accuracy of the quadrotor after introducing the dynamics model are increased by more than 5.5%, 3.1%, and 10.7%, respectively. The estimation accuracy of roll, pitch, and yaw increased by more than 88.1%, 68.9%, and 66.4%, respectively, under large misalignment angles.

To autonomous systems (ASs) equipped with single-frequency GNSS chipsets, it is hard to fully use the high-precision carrier phase to assist positioning as the inherent integer ambiguity cannot be reliably obtained using only single-frequency observations, especially in obstructed environments. Although the time difference can be done to eliminate the integer ambiguity to form accurate constraint in graph based fusion, cycle slip is not properly handled in current methods, leading to degraded performance. In this paper, a performance enhancement of tightly coupled GNSS/IMU integration based on factor graph with robust time-differenced carrier phase (TDCP) loop closure is proposed. Different from traditional methods, cycle slip detection is firstly performed, and the effect of cycle slip can be eliminated in the formation of loop closure, which can bring about better robustness. Doppler frequency shift is used to detect cycle slip and carrier phase measurements at corresponding epochs are then selected to build TDCP loop closure. The proposed robust TDCP loop closure is fused with pseudorange, Doppler frequency shift and IMU via factor graph, which can make the full use of the absolute and relative measurements to enhance the performance of tightly coupled GNSS/IMU integration. Simulation and field tests are conducted to validate the proposed method. The results show that the proposed method can improve the positioning performance with robust TDCP loop closure. Meanwhile, the computational load of proposed method is discussed.

The main error in traditional inertial pedestrian navigation is caused by unobservable heading errors with only zero velocity constraints. In this article, a human posture model-aided optimized fusion framework based on dominant events for pedestrian navigation is proposed to precisely locate pedestrians. Optimization is performed only at the moment of dominant events to reduce computational effort and improve the robustness. This model can fit the preferred heading information between two dominant events for fusion to improve accuracy. The optimization-based framework can also reduce the nonlinear error by relinearizing the state and associating constraints to the historical state to estimate the biases better. Besides, a hybrid visual heading angle estimation method is proposed by leveraging the Manhattan world hypothesis to obtain a drift-free heading angle with a monocular camera. The hypothesis is searched in the designated region based on histogram quality assessment that can improve the robustness and significantly reduce the computational amount. With low-cost devices, the multiple indoor experiments show that the proposed method can achieve 0.11% positioning accuracy comparable to traditional algorithms, which require numerous conditions to be met in good environments, and achieving over 70% improvement in accuracy over traditional methods in complex environments.