The Gauss-Markov theorem states that achieving an optimal solution requires homoscedasticity of error terms. However, during camera calibration, target placement affects image clarity and feature scale, leading to varying localization variances and suboptimal calibration results. Existing methods assume uniform localization variance within the depth of field, but this assumption breaks down in wide-range measurement scenarios where the target moves beyond the depth of field, degrading calibration accuracy and measurement precision. To address this issue, a wide-range camera calibration method based on normalized localization variance is proposed. A localization variance model for checkerboard corner is established, defining the relationship between image processing scale and localization variance, from which the normalized localization variance is derived. This normalized localization variance is incorporated into the nonlinear optimization of calibration parameters to ensure compliance with the Gauss-Markov theorem and achieve theoretical optimality. Experimental results validate the effectiveness of the proposed method, demonstrating reduced measurement errors and improved calibration accuracy across various applications, including satellite reconstruction and pose measurement application.

Robotic navigation tasks require multi-dimensional sensors collaboration for environmental perception, where data fusion between camera and lidar plays a critical role in 2D-3D data fusion. However, the reliability of current 2D-3D sensors fusion approaches is limited by the accuracy, efficiency, and working range of joint sensors calibration. To address the challenges posed by the complexity and wide-range requirements of 2D-3D sensors calibration, this paper analyzes the characteristics of 2D and 3D data and proposes a novel multi-feature calibration target. This target integrates both checkerboard patterns and spherical markers, levesraging the camera’s sensitivity to planar features and the lidar’s ability to perceive spatial structures, thus enabling high-precision feature extraction for both sensors modalities. In addition, spatial alignment between the measurement domains of the two sensors is performed to achieve high-precision calibration of structural parameters across the entire measurement range. Experimental results demonstrate that the proposed joint calibration method achieves high accuracy and provides a reliable calibration foundation for robotic navigation and other 2D-3D sensors fusion tasks.

Accurate Perspective-n-Point (PnP) pose measurement for space targets benefits from consistency between the topological structure of extracted two-dimensional feature points and that of projected three-dimensional template points. Existing keypoint extraction methods mainly optimize individual point localization accuracy, while paying insufficient attention to the global topological consistency of the keypoint set under relative-position relations, structural dependencies, and rigid projection constraints. As a result, frontend keypoint errors can be further amplified by the backend PnP solver. To address this problem, this paper proposes a topological-projection-consistent keypoint extraction method for PnP-based space-target pose measurement under extreme illumination. First, a Residual-Gated Context-Chained Enhancement Module (RG-CCEM) is introduced to build global structural feature propagation in degraded images. During distillation, both two-dimensional pairwise structural constraints and three-dimensional weak-perspective template reprojection constraints are imposed so that the student network learns keypoint sets whose projected topology remains consistent with the rigid 3D template. A reliability–reprojection-residual jointly reweighted PnP method is then used to reduce the influence of topologically inconsistent correspondences on pose estimation. Experiments show that the proposed method improves the stability of keypoint topology and pose measurement accuracy under extreme illumination. Compared with the DETRPose baseline, the proposed method decreases the outlier-keypoint ratio from 21.0% to 9.2%, and reduces rotation, translation, and camera-center RMSE by approximately 55.1%, 39.8%, and 56.3%, respectively, on our laboratory semi-physical dataset. Limitations of this method are further discussed.

Dense 3D reconstruction and accurate pose measurement of non-cooperative satellites are essential for on-orbit servicing missions. Multi-view stereo (MVS) is the preferred approach for high-precision dense reconstruction from monocular imagery, yet current methods struggle with the speed–accuracy trade-off and reconstruction degradation under complex on-orbit conditions, while keypoint-based pose measurement is further constrained by limited training data and heavy reliance on manual annotations. To address these challenges, we present a monocular vision-based method coupling dense 3D reconstruction with pose estimation. We develop MambaMVS, an end-to-end MVS network featuring a Mamba Cost Volume (MCV) Module for efficient cost volume regularization via the linear complexity of structured state space models, and an improved focused linear attention module for robust cross-view feature matching. The reconstructed point cloud provides 3D keypoint coordinates for pose measurement, where a few-shot scheme employing 3D Gaussian Splatting-based view augmentation and reprojection-driven self-labeling enables training with minimal manual annotations. During online inference, detected 2D keypoints are combined with offline-reconstructed 3D coordinates via the Perspective-n-Point algorithm for fast solving of the 6-DOF pose. Experiments on the Customized Space Target dataset demonstrate state-of-the-art reconstruction quality with 1.5× faster inference than current leading methods. Physical satellite model experiments achieve rotation ME within 0.2° and translation ME below 0.01 m, while robustness is validated across varying illumination, reflective surfaces, motion blur, and occlusion. The proposed reconstruction-guided measurement paradigm offers a practical pathway toward autonomous on-orbit perception and in situ structural assessment.