This paper presents a design method for axial and radial uniform field coils in a magnetically shielded cylinder (MSC), which suppresses degradation of magnetic field uniformity caused by ferromagnetic coupling while satisfying coil constant constraints. First, a coupled analytical model for the coil magnetic field inside the ferromagnetic boundary of the MSC is established based on Green’s function expansion and magnetic boundary condition. Then, a parameter optimization model is formulated with the uniformity within the target area as the objective and the coil constant as the constraint. The covariance matrix adaptation evolution strategy is adopted to optimize the coil structural parameters, enabling high-uniformity designs of axial and radial coils. Finally, the effectiveness of the proposed method is validated through simulations and experimental tests. Compared with the design without coupling consideration, the proposed method decreases the maximum relative deviation of the magnetic field in the target region by 1–2 orders of magnitude while satisfying the coil constant constraints. The maximum relative deviations of the axial and radial uniform field coils are 3.8×10−4 and 1.5×10−4, respectively. This method provides support for designing high-uniformity calibration coils and active compensation coils in weak magnetic measurement systems.
To significantly reduce the size and weight of airborne rotational inertial navigation systems (RINS), next-generation architectures eliminate conductive slip-rings. However, this imposes strict ±180° rotation limits, rendering traditional continuous-rotation calibration methods physically unexecutable. Furthermore, conventional full-state filters suffer from catastrophic cross-covariance contamination when extracting subtle temperature-dependent errors within a brief 1-hour cold-start window. To overcome these coupled physical and mathematical bottlenecks, this paper proposes a rapid, multi-parameter in-situ self-calibration framework for lightweight slipring-free RINS. The proposed methodology achieves break-throughs through three key innovations: (1) An optimized slipring-free kinematic excitation sequence utilizing strategic reversal maneuvers to maximize parameter observability within mechanical limits; (2) A novel Staged Decoupling architecture that acts as a structural mathematical firewall, protecting weakly observable states from initial massive transient errors; and (3) A Backtracking mechanism that leverages data reuse to mathematically extract temperature-dependent coefficients utilizing the fleeting natural thermal transient. Generalized numerical simulations and long-endurance in-vehicle dynamic tests unequivocally demonstrate that the proposed framework completely resolves high-dimensional covariance collapse, improving pure inertial positioning accuracy by an average of 26.1% compared to degraded factory baselines. This advancement enables robust, high-precision autonomous navigation in weight-constrained, GNSS-denied environments without requiring specialized thermal chamber recalibration.
Visual simultaneous localization and mapping (VS-LAM) is fundamental for autonomous perception and navigation. However, existing systems often suffer from degraded localization and reconstruction performance in highly dynamic scenes, weak-texture regions, and illumination variations. To address these challenges, this paper proposes DANI-SLAM, a dynamic-aware neural implicit SLAM framework that integrates learning-based feature representation, dynamic scene understanding, and neural implicit mapping into a unified pipeline for robust pose estimation and reliable scene reconstruction. Specifically, DANI-SLAM employs a dynamic-aware keyframe selection strategy to suppress unreliable observations and stabilize front-end tracking. An uncertainty-aware loop closure mechanism is introduced by incorporating pose covariance into candidate retrieval and re-ranking, improving loop reliability and global consistency. Furthermore, an uncertainty-guided pixel-level neural implicit sub-map fusion strategy is designed to suppress inconsistent observations caused by dynamic objects or occlusions, resulting in more complete and geometrically consistent reconstructions. Extensive experiments on four public datasets, including the TUM RGB-D, Replica, ScanNet, and Bonn RGB-D datasets, demonstrate that DANI-SLAM consistently outperforms representative traditional and learning-based SLAM methods in both localization and reconstruction tasks. In highly dynamic TUM RGB-D sequences, DANI-SLAM achieves up to 48% lower ATE RMSE than baseline methods, while also obtaining superior reconstruction quality in terms of Depth L1, PSNR, SSIM, and LPIPS. These results validate the robustness, generalization, and effectiveness of the proposed system. The source code is available at: https://github.com/DANI-SLAM/DANI-SLAM.
Multi-camera imaging and camera arrays have become ubiquitous in many applications, such as autonomous driving, robot control, or virtual reality, and accurate disparity maps of objects and their environment are essential for reliable operation. Despite recent advances in neural networks, correctly estimating the disparity of flat and textureless objects remains challenging. In particular, we consider a scenario defined by flat, textureless surfaces illuminated by a single fixed light source, resulting in one dominant specular reflection visible on the object surface. Under these conditions, reliable geometric and photometric cues are missing, and the specular reflection often causes mispredictions, especially when using conventional methods that rely on texture information to match corresponding pixels. To address this issue, the novel Specular Reflection Disparity Estimation SRDE algorithm is introduced, which is specifically designed for the constrained scenario of planar, textureless objects and single-source illumination. Unlike conventional stereo matching methods, SRDE ignores texture and instead leverages the geometric properties of specular reflections by incorporating the position information of the reflective region, the light source, and the camera setup. We show that SRDE outperforms existing methods by a notable margin, achieving more than a 52
