Accurate and robust alignment between LiDAR and visual sensors is essential for sensor fusion in perception-critical environments. However, it remains challenging due to confusing points, occlusion issues, and measurement noises inherent in mechanical LiDAR systems. Misalignments between 3D points and pixels can significantly impair system perception, underscoring the need for reliable calibration in automated tasks. This study addresses these challenges by introducing a specially designed calibration board that offers rich geometric elements, enabling adaptive constraints for more stable primitive extraction in both image and point cloud. Compared with existing checkerboards, the proposed board supports a wider effective calibration range and reduces sensitivity to sensor setup and viewing angles, especially for sparse LiDAR. A detailed analysis of the two fundamental assumptions underlying existing calibration frameworks is conducted. To address the associated limitations, an enhanced Region Growing (RG) algorithm is developed integrated with spatial feature analysis (SFA) is proposed, which improves the accuracy and robustness of geometric feature extraction under noisy or occluded conditions. Experimental results demonstrate that the proposed method achieves comparable rotation and translation accuracy to existing target-based techniques, while providing greater operational convenience and a wider effective range of 1.2 to 6.5 meters for the 16-line LiDAR and monocular camera setup.

Most existing shallow semi-supervised domain adaptation (SSDA) algorithms are based mainly on the framework adopting the maximum mean discrepancy (MMD) criterion, which is unstable and easily becomes stuck in a poor local minimum. Moreover, existing SSDA methods typically assume that the influence of the source domain is equivalent to that of the target domain, which is unreasonable and severely limits their performance. To address such drawbacks, we propose a novel SSDA framework derived from simple least squares regression (LSR) in a joint transductive and inductive learning paradigm, named transferable LSR (TLSR). Specifically, TLSR first learns domain-shared features using transfer component analysis (TCA) in a transductive paradigm. Then, TLSR augments the TCA features into the raw sample feature, formulating them into a block-diagonal matrix and training them in an inductive learning paradigm. This joint transductive and inductive learning paradigm helps alleviate the negative impacts of the MMD criterion of TCA but preserves the useful learned domain-shared knowledge. Moreover, the proposed block-diagonal input structure helps to separate the learned projections into independent domain-specific parts. Owing to the block-diagonal input structure, the influence of each domain can be reweighted, leading to significant improvements in performance. The experimental results demonstrate that the proposed TLSR outperforms the other shallow state-of-the-art competitors in 68 out of 90 cross-domain tasks. The source code of TLSR is available at:https://github.com/Evelhz/TLSR.

Feature Descriptors and Detectors are two main components of feature-based point cloud registration. However, little attention has been drawn to the explicit representation of local and global semantics in the learning of descriptors and detectors. In this paper, we present a framework that explicitly extracts dual-level descriptors and detectors and performs coarse-to-fine matching with them. First, to explicitly learn local and global semantics, we propose a hierarchical contrastive learning strategy, training the robust matching ability of high-level descriptors, and refining the local feature space using low-level descriptors. Furthermore, we propose to learn dual-level saliency maps that extract two groups of keypoints in two different senses. To overcome the weak supervision of binary matchability labels, we propose a ranking strategy to label the significance ranking of keypoints, and thus provide more fine-grained supervision signals. Finally, we propose a global-to-local matching scheme to obtain robust and accurate correspondences by leveraging the complementary dual-level features. Quantitative experiments on 3DMatch and KITTI odometry datasets show that our method achieves robust and accurate point cloud registration and outperforms recent keypoint-based methods. [code release]

Abstract The intelligentisation of coal mines is the only approach to the high‐quality development of the coal industry. Detection, identification and sorting of coal gangue is an important part of the intelligentisation of coal mines. Focusing on various problems in coal gangue detecting and recognising algorithms, such as limited receptive field, slow convergence rate and low accuracy of small particle recognition, this paper proposes a coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 (DCN‐YOLOv3). To improve the accuracy of anchor frame positioning and enhance the diversity of the dataset, the deformed convolution YOLOv3 network model is established based on the detection algorithm YOLOv3, using deformable convolution, multiple k‐means clustering results average method and data enhancement technology as means. The model was trained through the self‐designed dataset, and the algorithm's correctness and accuracy for coal gangue recognition under different size and illumination conditions are verified. The test results showed that the algorithm effectively detects and recognises coal gangue, improves the accuracy and efficiency of detecting and recognising small‐size coal and gangue and improves environmental robustness. Furthermore, compared with the traditional recognition algorithm, the network convergence speed of this algorithm is significantly improved, the mAP is increased to 99.45%, and the maximum FLOPs value is reduced by 61.4%. Accordingly, this research is considered to be of certain theoretical value and technical reference for identifying coal gangue.