Non-repetitive scanning Light Detection And Ranging(LiDAR)-Camera systems are commonly used in autonomous navigation industries, benefiting from their low-cost and high-perception characteristics. However, due to the irregular scanning pattern of LiDAR, feature extraction on point cloud encounters the problem of non-uniformity distribution of density and reflectance intensity, accurate extrinsic calibration remains a challenging task. To solve this problem, this paper presented an open-source calibration method using only a printed chessboard. We designed a two-stage coarse-to-fine pipeline for 3D corner extraction. Firstly, a Gaussian Mixture Model(GMM)-based intensity cluster approach is proposed to adaptively identify point segments in different color blocks of the chessboard. Secondly, a novel Iterative Lowest-cost Pose(ILP) algorithm is designed to fit the chessboard grid and refine the 3D corner iteratively. This scheme is unique for turning the corner feature extraction problem into a grid align problem. After the corresponding 3D-2D points are solved, by applying the PnP(Perspective-n-Point) method, along with nonlinear-optimization refinement, the extrinsic parameters are obtained. Extensive simulation and real-world experimental results show that our method achieved subpixel-level precision in terms of reprojection error. The comparison demonstrated that the effectiveness and accuracy of the proposed method outperformed existing methods.

Using unmanned aerial vehicles (UAVs) for remote sensing has the advantages of high flexibility, convenient operation, low cost, and wide application range. It fills the need for rapid acquisition of high-resolution aerial images in modern photogrammetry applications. Due to the insufficient parallaxes and the computation-intensive process, dense real-time reconstruction for large terrain scenes is a considerable challenge. To address these problems, we proposed a novel SLAM-based MVS (Multi-View-Stereo) approach, which can incrementally generate a dense 3D (three-dimensional) model of the terrain by using the continuous image stream during the flight. The pipeline of the proposed methodology starts with pose estimation based on SLAM algorithm. The tracked frames were then selected by a novel scene-adaptive keyframe selection method to construct a sliding window frame-set. This was followed by depth estimation using a flexible search domain approach, which can improve accuracy without increasing the iterate time or memory consumption. The whole system proposed in this study was implemented on the embedded GPU based on an UAV platform. We proposed a highly parallel and memory-efficient CUDA-based depth computing architecture, enabling the system to achieve good real-time performance. The evaluation experiments were carried out in both simulation and real-world environments. A virtual large terrain scene was built using the Gazebo simulator. The simulated UAV equipped with an RGB-D camera was used to obtain synthetic evaluation datasets, which were divided by flight altitudes (800-, 1000-, 1200 m) and terrain height difference (100-, 200-, 300 m). In addition, the system has been extensively tested on various types of real scenes. Comparison with commercial 3D reconstruction software is carried out to evaluate the precision in real-world data. According to the results on the synthetic datasets, over 93.462% of the estimation with absolute error distance of less then 0.9%. In the real-world dataset captured at 800 m flight height, more than 81.27% of our estimated point cloud are less then 5 m difference with the results of Photoscan. All evaluation experiments show that the proposed approach outperforms the state-of-the-art ones in terms of accuracy and efficiency.

Convenient and high-fidelity 3D model reconstruction is crucial for industries like manufacturing, medicine and archaeology. Current scanning approaches struggle with high manual costs and the accumulation of errors in large-scale modeling. This paper is dedicated to achieving industrial-grade seamless and high-fidelity 3D reconstruction with minimal manual intervention. The innovative method proposed transforms the multi-frame registration into a graph optimization problem, addressing the issue of error accumulation encountered in frame-by-frame registration. Initially, a global consistency cost is established based on point cloud cross-multipath registration, followed by using the geometric and color differences of corresponding points as dynamic nonlinear weights. Finally, the iteratively reweighted least squares (IRLS) method is adopted to perform the bundle adjustment (BA) optimization of all poses. Significantly enhances registration accuracy and robustness under the premise of maintaining near real-time efficiency. Additionally, for generating watertight, seamless surface models, a local-to-global transitioning strategy for multiframe fusion is introduced. This method facilitates efficient correction of normal vector consistency, addressing mesh discontinuities in surface reconstruction resulting from normal flips. To validate our algorithm, we designed a 3D reconstruction platform enabling spatial viewpoint transformations. We collected extensive real and simulated model data. These datasets were rigorously evaluated against advanced methods, roving the effectiveness of our approach. Our data and implementation is made available on GitHub for community development.

In the realm of autonomous driving, there is a pressing demand for heightened perceptual capabilities, giving rise to a plethora of multisensory solutions. Among these, multi-LiDAR systems have gained significant popularity. Within the spectrum of available combinations, the integration of repetitive and non-repetitive LiDAR configurations emerges as a balanced approach, offering a favorable trade-off between sensing range and cost. However, the calibration of such systems remains a challenge due to the diverse nature of point clouds, low-common-view, and distinct densities. This study proposed a novel targetless calibration algorithm for extrinsic calibration between Hybrid-Solid-State-LiDAR(SSL) and Mechanical-LiDAR systems, each employing different scanning modes. The algorithm harnesses planar features within the scene to construct matching costs, while proposing the adoption of the Gaussian Mixture Model (GMM) to address outliers, thereby mitigating the issue of overlapping points. Dynamic trust-region-based optimization is incorporated during iterative processes to enhance nonlinear convergence speed. Comprehensive evaluations across diverse simulated and real-world scenarios affirm the robustness and precision of our algorithm, outperforming current state-of-the-art methods.

Vision-related state estimation usually extracts multiple feature points from images captured by the camera. In this paper, we propose a robust feature homogenization method for resolving the problem of feature clustering. The proposed method deduced the depth of feature points from optical flow magnitude, and the homogenization of feature points was acquired by adaptively enforcing the minimum distance between neighboring feature points. With the assistance of optical flow, the proposed method develops a preference for feature points with smaller depths in feature homogenization. Experimental results show that the proposed method improves the system’s global consistency and tracking stability by using optical flow information.