The current navigation systems used in many autonomous mobile robotic applications, like unmanned vehicles, are always equipped with various sensors to get accurate navigation results. The key point is to fuse the information from different sensors efficiently. However, different sensors provide asynchronous measurements, some of which even appear to be nonlinear. Moreover, some sensors are vulnerable in specific environments, e.g., GPS signal is likely to work poorly in interior space, underground, and tall buildings. We propose a multi-sensor information fusion method based on a factor graph to fuse all available asynchronous sensor information and efficiently and accurately calculate a navigation solution. Assuming the sensor measurements and navigation states in a navigation system as factor nodes and variable nodes in a factor graph, respectively, the update of the states can be implemented in the framework of the factor graph. The proposed method is experimentally validated using two different datasets. A comparison with Federated Filter, which has been widely used in integrated navigation systems, demonstrates the proposed method’s effectiveness. Additionally, analyzing the navigation results with data loss verifies that the proposed method could achieve sensor plug and play in software.

An inertial navigation system (INS) has been widely used in challenging GPS environments. With the rapid development of modern physics, an atomic gyroscope will come into use in the near future with a predicted accuracy of 5 × 10⁻⁶°/h or better. However, existing calibration methods and devices can not satisfy the accuracy requirements of future ultra-high accuracy inertial sensors. In this paper, an improved calibration model is established by introducing gyro g-sensitivity errors, accelerometer cross-coupling errors and lever arm errors. A systematic calibration method is proposed based on a 51-state Kalman filter and smoother. Simulation results show that the proposed calibration method can realize the estimation of all the parameters using a common dual-axis turntable. Laboratory and sailing tests prove that the position accuracy in a five-day inertial navigation can be improved about 8% by the proposed calibration method. The accuracy can be improved at least 20% when the position accuracy of the atomic gyro INS can reach a level of 0.1 nautical miles/5 d. Compared with the existing calibration methods, the proposed method, with more error sources and high order small error parameters calibrated for ultra-high accuracy inertial measurement units (IMUs) using common turntables, has a great application potential in future atomic gyro INSs.

This article investigates an encoder-based relative attitude observation method for self-calibration in a dual-axis rotational inertial navigation system (RINS). First, the calibration error of the gyroscope parameters caused by the velocity and position observations in systematic calibration is analyzed. Second, a relative attitude observation method utilizing photoelectric encoders for gyroscope parameter estimation is proposed. We apply the proposed method in our designed dual-axis RINS to verify its effectiveness in this article. Experimental results show that the calibration accuracy of the gyroscope can be improved to 0.0005 $^ \circ \text{/h}$ in bias, and 0.98 ppm in scale factor using the proposed method. The proposed self-calibration method can be utilized as a high-accuracy self-calibration method in the dual-axis RINS when the vehicle is in static condition.

This article investigates a novel Bluetooth odometer (BOD)-aided smartphone-based vehicular navigation system for satellite-denied environments. First, a wheel-mounted BOD is designed to measure rotational speed and send data to smartphones by Bluetooth transmission. Second, a nonlinear smartphone-based microelectromechanical system (MEMS) inertial measurement unit (IMU)/BOD error model is derived to solve the problem of azimuth divergence. Then, a position delay time model is proposed to compensate for the delay time of Bluetooth transmission. An Android app is developed to receive the BOD data from Bluetooth transmission, and the proposed BOD smartphone-based vehicular navigation algorithm provides positioning information in real time. In field tests, positioning accuracy reaches 0.38%D by using the proposed MEMS-IMU/BOD model. The designed scheme can be used as a low-cost and convenient solution for smartphone-based civilian vehicular autonomous navigation systems without changing the habits of drivers.

The cooperative aerial-ground robotic team attracts increasing attention for diverse Internet-of-Things (IoT) applications. A navigation system is essential for the team to collectively localize itself and execute missions. In this paper, we propose an autonomous information-seeking navigation method in global navigation satellite system (GNSS) denied environments. Specifically, we first provide a theoretical analysis of the proposed navigation scheme in terms of the Cramer-Rao low bound (CRLB). Then, we propose a motion strategy with weighted position matrix, which allows the robotic team to actively adapt its position to enriching position information. Moreover, a belief propagation (BP) based algorithm is developed for the proposed scheme to improve the localization accuracy by alleviating the accuracy degradation caused by accumulated errors in inertial measurements. Numerical simulations reveal the advantages of the proposed methods, providing meaningful insights and performance evaluations for system implementations.