The nine-axis MEMS Inertial Measurement Units (IMU) have been widely used in various fields, such as underwater vehicles, unmanned aerial vehicles and bionic robots. Due to the noises of gyroscope sensors and errors introduced in solution process, the rotation angles estimated using only angular velocity data usually contain large accumulated errors and have to be corrected by acceleration and geomagnetic measurements. A serious problem is if there is strong magnetic anomaly field in the environment, the geomagnetic field aiding performance decrease quickly and probably leads to extra errors. To improve the heading and attitude estimation accuracy of the nine-axis MEMS IMU in magnetic anomaly field, a partially adaptive Extended Kalman Filter (PADEKF) using double quaternions is proposed in this work. To reduce the coupled influence of magnetic measurement noise on attitude estimation in a single quaternion, the heading and attitude angles are represented with two independent quaternions in the state vector. Self-adaptability design is adopted in the EKF to improve the robustness to spatially varying magnetic anomaly data. For the case that the strong and quickly varying magnetic anomaly field cannot be well modelled by the PADEKF, a combination algorithm of long and short-term memory (LSTM) neural network and Runge-Kutta method is given to make good heading estimation. Field experiments in different scenarios are performed and verified the effectiveness of the proposed approach.

Due to the position drift of inertial navigation systems, it is still challenging to achieve long-term and accurate position estimates during underwater navigation. The seabed topography has been proven to be effective in aiding information for accurate positioning benefiting from its rich spatial variation. With the advantage of the multibeam echosounder (MBES) in efficient bathymetric survey, the simultaneous localization and mapping (SLAM) approach can be performed using bathymetric data in unknown environments for underwater vehicles to get good position estimates. The SLAM performance relies on the number and accuracy of loop closures heavily. Thereby, the capabilities of the data association method and solver in dealing with the uncertainties of vehicle pose estimates, bathymetric data, and topographic features affect the SLAM performance strongly. This work proposes a new graph-based bathymetric SLAM method to improve the robustness of the uncertainties in both factor-graph construction and optimization stages. In the front end, on the base of a matching suitability-based MBES submap construction method, a dual-stage bathymetric point cloud registration approach that is able to detect most false loop closures is proposed. In the back end, a robust optimizer based on Frechet distance is introduced to further identify and remove the false loop closures missed in front end. Experiments using field MBES bathymetric data sets are conducted to verify the effectiveness of the proposed approach.

Accurate positioning is the necessary basis for autonomous underwater vehicles (AUV) to perform safe navigation in underwater tasks, such as port environment monitoring, target search, and seabed exploration. The position estimates of underwater navigation systems usually suffer from an error accumulation problem, which makes the AUVs difficult use to perform long-term and accurate underwater tasks. Underwater simultaneous localization and mapping (SLAM) approaches based on multibeam-bathymetric data have attracted much attention for being able to obtain error-bounded position estimates. Two problems limit the use of multibeam bathymetric SLAM in many scenarios. The first is that the loop closures only occur in the AUV path intersection areas. The second is that the data association is prone to failure in areas with gentle topographic changes. To overcome these problems, a joint graph-based underwater SLAM approach that fuses bathymetric and magnetic-beacon measurements is proposed in this paper. In the front-end, a robust dual-stage bathymetric data-association method is used to first detect loop closures on the multibeam bathymetric data. Then, a magnetic-beacon-detection method using Euler-deconvolution and optimization algorithms is designed to localize the magnetic beacons using a magnetic measurement sequence on the path. The loop closures obtained from both bathymetric and magnetic-beacon observations are fused to build a joint-factor graph. In the back-end, a diagnosis method is introduced to identify the potential false factors in the graph, thus improving the robustness of the joint SLAM system to outliers in the measurement data. Experiments based on field bathymetric datasets are performed to test the performance of the proposed approach. Compared with classic bathymetric SLAM algorithms, the proposed algorithm can improve the data-association accuracy by 50%, and the average positioning error after optimization converges to less than 10 m.