In the streak tube laser imaging system, there are two conflicts: the first is between high spatial resolution and wide field of view (FOV). The second is between high temporal resolution and deep depth of field (DOF). In this paper, a new non-scanning streak tube laser imaging system is presented. A microlens array with three different apertures is used to non-uniformly collect data from the image plane so the conflict between high spatial resolution and wide FOV is relieved and the detectable range of system is also increased. A remapping fiber optics with special design is used to realign the image plane on the two photocathodes of streak tubes to realize the operation mode of the two streak tubes so the conflict between high temporal resolution and deep DOF is relieved. The mathematical model of the entire imaging system is established based on the range equation. The structure parameters of the receiving optical system are optimized in order to achieve the optimal utilization rate of light energy. In the third, three simulated contrast experiments are organized, and the experiment results demonstrate that the imaging system proposed in this paper possesses properties of higher spatial resolution, wider FOV, higher temporal resolution, deeper DOF, and larger detectable range.

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.

Although the Geiger-mode avalanche photodiode (GM-APD) flash ladar system offers the advantages of high sensitivity and simple construction, its detection performance is influenced not only by the incoming signal-to-noise ratio but also by the absolute number of noise photons. In this paper, we deduce a hyperbolic approximation to estimate the noise-photon number from the false-firing percentage in a GM-APD flash ladar system under dark conditions. By using this hyperbolic approximation function, we introduce a method to adapt the aperture to reduce the number of incoming background-noise photons. Finally, the simulation results show that the adaptive-aperture method decreases the false probability in all cases, increases the detection probability provided that the signal exceeds the noise, and decreases the average ranging error per frame.

Combined with the characteristic of the multi-slit streak tube imaging LiDAR (MS-STIL) system where the imaging areas corresponding to each slit do not interfere with each other, we denoised the streak images by an improved fast non-local mean filtering algorithm. Experiments were performed to investigate the effectiveness of the method. The experimental results show that the spatial resolution of the system is improved from 22 to 16 mm; the relative distance error is reduced by an average of 22.76%; and the intensity accuracy improved significantly when the distance is 10 m. Additionally, the overall denoising effect is comprehensively verified by long-range target imaging. The mean square error of the reconstructed depth image and intensity image are reduced from 0.0836, 0.0067 to 0.0433, 0.0037, respectively. The applicability of the proposed method was verified through comparative experiments in different environments.