Vision sensors are extensively used for localizing a robot's pose, particularly in environments where global localization tools such as GPS or motion capture systems are unavailable. In many visual navigation systems, localization is achieved by detecting and tracking visual features or landmarks, which provide information about the sensor's relative pose. For reliable feature tracking and accurate pose estimation, it is crucial to maintain visibility of a sufficient number of features. This requirement can sometimes conflict with the robot's overall task objective. In this paper, we approach it as a constrained control problem. By leveraging the invariance properties of visibility constraints within the robot's kinematic model, we propose a real-time safety filter based on quadratic programming. This filter takes a reference velocity command as input and produces a modified velocity that minimally deviates from the reference while ensuring the information score from the currently visible features remains above a user-specified threshold. Numerical simulations demonstrate that the proposed safety filter preserves the invariance condition and ensures the visibility of more features than the required minimum. We also validated its real-world performance by integrating it into a visual simultaneous localization and mapping (SLAM) algorithm, where it maintained high estimation quality in challenging environments, outperforming a simple tracking controller.

In autonomous driving, a prior LiDAR map(PLM) is used as a powerful tool for correcting SLAM drift, but finding robust and accurate correspondences between cross modal sensors is a challenging problem. To address this cross-modality issue, this paper proposes a real-time plane-based stereo localization system with a PLM. In the proposed system, drift in visual pose estimation is eliminated through plane-based joint optimization and the registration module. Two types of planes are employed in this system: surfel, ensuring accuracy in a narrow domain, and global plane, providing robustness in a wide area. For accurate and robust matching between the visual map point and PLM, surfels and global planes in PLM are utilized collaboratively based on point-to-PLM distance. To reduce the computational cost of the registration module, a plane-to-plane drift estimation module is proposed. The performance of the proposed system is extensively validated across synthetic simulation and real-world indoor and outdoor datasets. We validate the effectiveness of each module through ablation studies and also assess the robustness against error that may exist in PLM and initial pose. In most of the validation, the proposed system shows more accurate and robust performance compared to the state-of-the-art methods.

This paper proposes an automatic and accuracy- enhanced extrinsic calibration method for 3D LiDARs with a range offset correction, which needs only an arbitrarily-shaped single planar board. One of the most exhaustive parts of existing LiDAR calibration procedures is to manually find target objects from massive point clouds. To obviate user interventions, we propose an automated planar board detection from LiDAR range images. To extract a target completely, we suppress outliers and restore rejected inliers of the target board by introducing a target completion method. We empirically find that range measurements of various LiDARs are mainly skewed by constant offset values. To compensate for this, we suggest a range offset model for each laser channel in calibration procedures. The relative pose between LiDARs and range offsets are jointly estimated by minimizing bi-directional point- to-board distances within the iterative re-weighted least squares (IRLS) framework. To verify the suggested range offset model, we obtain and analyze extensive real-world measurements. By conducting experiments using the various sensor configurations and shapes of boards, we quantitatively and qualitatively confirm accuracy and versatility of the proposed method by comparing with the state-of-the-art LiDAR calibration methods. All the source code and data used in the paper are available at : https://github.com/JunhaAgu/AutoL2LCalib.