A Method for SINS Alignment with Large Initial Misalignment Angles Based on Kalman Filter with Parameters Resetting

In the initial alignment process of strapdown inertial navigation system (SINS), large initial misalignment angles always bring nonlinear problem, which causes alignment failure when the classical linear error model and standard Kalman filter are used. In this paper, the problem of large misalignment angles in SINS initial alignment is investigated, and the key reason for alignment failure is given as the state covariance from Kalman filter cannot represent the true one during the steady filtering process. According to the analysis, an alignment method for SINS based on multiresetting the state covariance matrix of Kalman filter is designed to deal with large initial misalignment angles, in which classical linear error model and standard Kalman filter are used, but the state covariance matrix should be multireset before the steady process until large misalignment angles are decreased to small ones. The performance of the proposed method is evaluated by simulation and car test, and the results indicate that the proposed method can fulfill initial alignment with large misalignment angles effectively and the alignment accuracy of the proposed method is as precise as that of alignment with small misalignment angles

H∞ filter for flexure deformation and lever arm effect compensation in M/S INS integration

ABSTRACTOn ship, especially on large ship, the flexure deformation between Master (M)/Slave (S) Inertial Navigation System (INS) is a key factor which determines the accuracy of the integrated system of M/S INS. In engineering this flexure deformation will be increased with the added ship size. In the M/S INS integrated system, the attitude error between MINS and SINS cannot really reflect the misalignment angle change of SINS due to the flexure deformation. At the same time, the flexure deformation will bring the change of the lever arm size, which further induces the uncertainty of lever arm velocity, resulting in the velocity matching error. To solve this problem, a H∞ algorithm is proposed, in which the attitude and velocity matching error caused by deformation is considered as measurement noise with limited energy, and measurement noise will be restrained by the robustness of H∞ filter. Based on the classical “attitude plus velocity” matching method, the progress of M/S INS information fusion is simulated and compared by using three kinds of schemes, which are known and unknown flexure deformation with standard Kalman filter, and unknown flexure deformation with H∞ filter, respectively. Simulation results indicate that H∞ filter can effectively improve the accuracy of information fusion when flexure deformation is unknown but non-ignorable

An Explicit Method for Fast Monocular Depth Recovery in Corridor Environments

Monocular cameras are extensively employed in indoor robotics, but their performance is limited in visual odometry, depth estimation, and related applications due to the absence of scale information.Depth estimation refers to the process of estimating a dense depth map from the corresponding input image, existing researchers mostly address this issue through deep learning-based approaches, yet their inference speed is slow, leading to poor real-time capabilities. To tackle this challenge, we propose an explicit method for rapid monocular depth recovery specifically designed for corridor environments, leveraging the principles of nonlinear optimization. We adopt the virtual camera assumption to make full use of the prior geometric features of the scene. The depth estimation problem is transformed into an optimization problem by minimizing the geometric residual. Furthermore, a novel depth plane construction technique is introduced to categorize spatial points based on their possible depths, facilitating swift depth estimation in enclosed structural scenarios, such as corridors. We also propose a new corridor dataset, named Corr\_EH\_z, which contains images as captured by the UGV camera of a variety of corridors. An exhaustive set of experiments in different corridors reveal the efficacy of the proposed algorithm.Comment: 10 pages, 8 figures. arXiv admin note: text overlap with arXiv:2111.08600 by other author

Cortical Neural Stem Cell Lineage Progression Is Regulated by Extrinsic Signaling Molecule Sonic Hedgehog.

Neural stem cells (NSCs) in the prenatal neocortex progressively generate different subtypes of glutamatergic projection neurons. Following that, NSCs have a major switch in their progenitor properties and produce γ-aminobutyric acid (GABAergic) interneurons for the olfactory bulb (OB), cortical oligodendrocytes, and astrocytes. Herein, we provide evidence for the molecular mechanism that underlies this switch in the state of neocortical NSCs. We show that, at around E16.5, mouse neocortical NSCs start to generate GSX2-expressing (GSX2+) intermediate progenitor cells (IPCs). In vivo lineage-tracing study revealed that GSX2+ IPC population gives rise not only to OB interneurons but also to cortical oligodendrocytes and astrocytes, suggesting that they are a tri-potential population. We demonstrated that Sonic hedgehog signaling is both necessary and sufficient for the generation of GSX2+ IPCs by reducing GLI3R protein levels. Using single-cell RNA sequencing, we identify the transcriptional profile of GSX2+ IPCs and the process of the lineage switch of cortical NSCs

Frequency-dependent orthotropic damping properties of Nomex honeycomb composites

In this paper, the orthotropic damping behavior of Nomex honeycomb composites and its causes are investigated. The needed specimen sizes for the measurement of the frequency-dependent transverse shear moduli (TSM) and fundamental damping coefficients of the honeycomb cores were analyzed at first. Then, the effects of cell side length and beam orientation on the orthotropic damping properties were explored. The results reveal that relatively high TSM (GLT) and damping values (ηWT) can be obtained by decreasing the cell side length without adding any additional weight. Damping mechanism analysis indicates that the difference in damping contribution of the interfacial phase to honeycomb core in different directions leads to the orthotropic damping behavior of honeycomb core. This study is helpful to guide the TSM measurement and structure design of honeycomb composites

An Extensible Positioning System for Locating Mobile Robots in Unfamiliar Environments

In this paper, an extensible positioning system for mobile robots is proposed. The system includes a stereo camera module, inertial measurement unit (IMU) and an ultra-wideband (UWB) network which includes five anchors, one of which is with the unknown position. The anchors in the positioning system are without requirements of communication between UWB anchors and without requirements of clock synchronization of the anchors. By locating the mobile robot using the original system, and then estimating the position of a new anchor using the ranging between the mobile robot and the new anchor, the system can be extended after adding the new anchor into the original system. In an unfamiliar environment (such as fire and other rescue sites), it is able to locate the mobile robot after extending itself. To add the new anchor into the positioning system, a recursive least squares (RLS) approach is used to estimate the position of the new anchor. A maximum correntropy Kalman filter (MCKF) which is based on the maximum correntropy criterion (MCC) is used to fuse data from the UWB network and IMU. The initial attitude of the mobile robot relative to the navigation frame is calculated though comparing position vectors given by a visual simultaneous localization and mapping (SLAM) system and the UWB system respectively. As shown in the experiment section, the root mean square error (RMSE) of the positioning result given by the proposed positioning system with all anchors is 0.130 m. In the unfamiliar environment, the RMSE is 0.131 m which is close to the RMSE (0.137 m) given by the original system with a difference of 0.006 m. Besides, the RMSE based on Euler distance of the new anchor is 0.061 m