The geomagnetic field constitutes a massive fingerprint database, and its unique structure provides potential position correction information. In recent years, particle filter technology has received more attention in the context of robot navigation. However, particle degradation and impoverishment have constrained navigation systems’ performance. This paper transforms particle filtering into a particle-matching positioning problem and proposes a geomagnetic localization method based on an adaptive fission particle filter. This method employs particle-filtering technology to construct a geomagnetic matching positioning model. Through adaptive particle fission and sampling, the problem of particle degradation and impoverishment in traditional particle filtering is solved, resulting in improved geomagnetic matching positioning accuracy. Finally, the proposed method was tested in a sea trial, and the results show that the proposed method has a lower positioning error than traditional particle-filtering and intelligent particle-filtering algorithms. Under geomagnetic map conditions, an average positioning accuracy of about 546.44 m is achieved.

With the rapid advancement of the simultaneous localization and mapping (SLAM) technology, the collaboration of several autonomous underwater vehicles (AUVs) in large-scale seafloor imaging has become a trending topic. Electromagnetic waves are difficult to transfer underwater, the only viable method of communication is acoustic transmission, but its bandwidth is limited. Therefore, how to compress and process multibeam bathymetry maps so that AUVs can acquire maps gathered by other AUVs has become an important topic of study. This study presents a representation approach for multibeam bathymetry maps based on a quadtree structure. In comparison to the girding approach, the sparse pseudo-input Gaussian processes (SPGPs) method, and the octree-based method, the quadtree-based method suggested in this study preserves precision while compressing storage space. Experiments utilizing field data validate the performance of the proposed technique, and the method’s ability to compress storage space towards an AUV cooperative SLAM’s scenario.

Inertial navigation systems (INSs) are a topical solution in underwater navigation. Although appealing due to their ability to estimate pose without external information, INS suffer from compounding position errors due to bias and random noise. In general, INSs require the assistance of other positioning devices to achieve satisfactory positioning results. To solve these problems, this paper proposes an ego-motion estimation framework with an inertial measurement unit (IMUs) and magnetic compass based on the deep learning theory. The main idea is to estimate the displacement of vehicles from the IMU data in the time window and combine this with magnetic compass headings to reconstruct the trajectories of the vehicles. The pre-integration technology is used to process raw IMU data, which mathematically separates the dependence of traditional inertial algorithms based on the initial value. Then convolutional neural networks (CNN) and attention hybrid networks are used to estimate the displacement of vehicles. In addition, the framework leverages the backpropagation neural network to fuse the magnetic heading and IMU measurements to obtain an accurate heading. Compared with other deep learning methods, the proposed method reduces computational complexity and improves position accuracy. Eventually, the accuracy of the proposed method is verified in the sea trail. The results show that the maximum value of absolute trajectory errors accounts for 12.8% of the distance in severe sea conditions and 6.38% in usual sea conditions.

Vector magnetometers play an essential role in aerial and marine navigation due to their advantages of low cost, portability, and high sensitivity. However, the unstable magnetic field anomaly restricts the performance improvement of the magnetic compass. The existing methods have done a lot of optimization and improvement work, but the test results are less than expected in a dynamic environment. In order to solve this problem, a bionic magnetic compass algorithm based on the theory of “radical pairs” in birds was proposed. This paper finds the stability convergence of the magnetic compass deviation (MCD) at the critical angle. The theoretical analysis and experiment results show that the heading accuracy at the critical angle is trustworthy. Then, the MCD distribution and magnetic heading correction model be established. Finally, the current magnetic heading is corrected according to the angle difference between the critical angle and the current heading. The sea trial shows that compared with the traditional compensation algorithm, reservoir environment is improved by 83.1%, and the effect in real ocean environment improved by 30.12% and 58.49%.

Accumulating evidence suggests that migrating animals store navigational “maps” in their brains, decoding location information from geomagnetic information based on their perception of the magnetic field. Inspired by this phenomenon, a novel geomagnetic inversion navigation framework was proposed to address the error constraint of a long-distance inertial navigation system. In the first part of the framework, the current paper proposed a geomagnetic bi-coordinate inversion localization approach which enables an autonomous underwater vehicle (AUV) to estimate its current position from geomagnetic information like migrating animals. This paper suggests that the combination of geomagnetic total intensity (F) and geomagnetic inclination (I) can determine a unique geographical location, and that there is a non-unique mapping relationship between the geomagnetic parameters and the geographical coordination (longitude and latitude). Then the cumulative error of the inertial navigation system is corrected, according to the roughly estimated position information. In the second part of the framework, a cantilever beam model is proposed to realize the optimal correction of the INS historical trajectory. Finally, the correctness of the geomagnetic bi-coordinate inversion localization model we proposed was verified by outdoor physical experiments. In addition, we also completed a geomagnetic/inertial navigation integrated long-distance semi-physical test based on the real navigation information of the AUV. The results show that the geomagnetic inversion navigation framework proposed in this paper can constrain long-distance inertial navigation errors and improve the navigation accuracy by 73.28% compared with the pure inertial navigation mode. This implies that the geomagnetic inversion localization will play a key role in long-distance AUV navigation correction.