Fully automated structured light scanning for high-fidelity 3D reconstruction via graph optimization.

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.

Extrinsic calibration for multi-LiDAR systems involving heterogeneous laser scanning models.

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.

Laser reflectance feature assisted accurate extrinsic calibration for non-repetitive scanning LiDAR and camera systems.

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.

Feature homogenization strategy using optical flow for vision-related simultaneous localization and mapping in a complex environment.

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.

Compact digital system for an iodine-stabilized laser.

A compact digital control system based on an all-programmable system-on-chip iodine-stabilized laser is presented for realization of the meter. The system is composed of ZYNQ7000, peripheral circuits, and human-computer interaction, which can operate independently. An nth-harmonic extraction algorithm with less resource consumption is used in this system. The digital system overcomes the problems of complex debugging, large volume, and manual locking. Additionally, customers can set up, calibrate, and upgrade the system by themselves. Its stability is similar to that of the current analog system, with long-term stability of up to 10-13. The repeatability of the two lasers with the digital system is approximately 1.5×10-11, and the absolute frequencies satisfy the international recommendation.

Target classification of multislit streak tube imaging lidar based on deep learning.

To reduce the impact of the image reconstruction process and improve the identification efficiency of the multislit streak tube imaging lidar (MS-STIL) system, an object classification method based on the echo of the MS-STIL system is proposed. A streak image data set is constructed that contains a total of 240 common outdoor targets in 6 categories. Additionally, the deep-learning network model based on ResNet is chosen to implement streak image classification. The effects of two classification methods based on streak images and reconstructed depth images are compared. To verify the maximum classification capability of the proposed method, the recognition effects are investigated under 6 and 20 classes. The results show that the classification accuracy decreases from 99.42% to 67.64%. After the data set is expanded, the classification accuracy improved to 85.35% when the class number of the target is 20.

Denoising of the multi-slit streak tube imaging LiDAR system using a faster non-local mean method.

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.

Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution Algorithm.

This work introduces a super-resolution (SR) algorithm for range images on the basis of self-guided joint filtering (SGJF), adding the range information of the range image as a coefficient of the filter to reduce the influence of the intensity image texture on the super-resolved image. A range image SR recognition system is constructed to study the effect of four SR algorithms including the SGJF algorithm on the recognition of the laser radar (ladar) range image. The effects of different model library sizes, SR algorithms, SR factors and noise conditions on the recognition are tested via experiments. Results demonstrate that all tested SR algorithms can improve the recognition rate of low-resolution (low-res) range images to varying degrees and the proposed SGJF algorithm has a very good comprehensive recognition performance. Finally, suggestions for the use of SR algorithms in actual scene recognition are proposed on the basis of the experimental results.

Fiber array coupling based multi-spectral streak tube detection imaging method.

The complementary advantages of the multi-spectral technique and streak tube detection imaging technique can enrich target information with high detection accuracy. In this paper, a new multi-spectral streak tube imaging method is proposed. Instead of using a set of receiving systems for each wavelength, i.e., the traditional method, we use different lengths of fiber arrays in our imaging system to produce the time difference between the echo signals of different wavelengths arriving at the detectors. The imaging process is completed by just one set of the receiving system. The contradiction between multiple wavelength information sub-area processing and large depth of field is alleviated, and the high time resolution is ensured. The mathematical model of this simplified system is established, and two simulation experiments are organized. First, the influence of the time difference on the judgment of echo signals is discussed. Second, we simulate the process of detecting and imaging a target using the traditional multi-spectral streak tube detection method and the proposed method. Finally, we compare the three-dimensional images obtained via the two methods. By observation and calculation, we find that the imaging depth of field and time resolution of the proposed method are better than those of the traditional method.

A new three-dimensional nonscanning laser imaging system based on the illumination pattern of a point-light-source array.

One of the most important goals of research on three-dimensional nonscanning laser imaging systems is the improvement of the illumination system. In this paper, a new three-dimensional nonscanning laser imaging system based on the illumination pattern of a point-light-source array is proposed. This array is obtained using a fiber array connected to a laser array with each unit laser having independent control circuits. This system uses a point-to-point imaging process, which is realized using the exact corresponding optical relationship between the point-light-source array and a linear-mode avalanche photodiode array detector. The complete working process of this system is explained in detail, and the mathematical model of this system containing four equations is established. A simulated contrast experiment and two real contrast experiments which use the simplified setup without a laser array are performed. The final results demonstrate that unlike a conventional three-dimensional nonscanning laser imaging system, the proposed system meets all the requirements of an eligible illumination system. Finally, the imaging performance of this system is analyzed under defocusing situations, and analytical results show that the system has good defocusing robustness and can be easily adjusted in real applications.

Adaptive aperture for Geiger mode avalanche photodiode flash ladar systems.

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.

Design and modeling of three-dimensional laser imaging system based on streak tube.

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.