Three-dimensional reconstruction technology has demonstrated broad application potential in the industrial, construction, medical, forestry, agricultural, and pastural sectors in the last few years. High-quality digital point cloud information exists to help researchers to understand objects and environments. However, current research mainly focuses on making adaptive adjustments to various scenarios and related issues in the application of this technology rather than looking for further improvements and enhancements based on technical principles. Meanwhile, a review of approaches, algorithms, and techniques for high-precision 3D reconstruction utilizing line-structured light scanning, which is analyzed from a deeper perspective of elementary details, is lacking. This paper takes the technological path as the logical sequence to provide a detailed summary of the latest development status of each key technology, which will serve potential users and new researchers in this field. The focus is placed on exploring studies reconstructing small-to-medium-sized objects, as opposed to performing large-scale reconstructions in the field.

The rapid developments of mobile robotics and autonomous navigation over the years are largely empowered by public datasets for testing and upgrading, such as sensor odometry and SLAM tasks. Impressive demos and benchmark scores have arisen, which may suggest the maturity of existing navigation techniques. However, these results are primarily based on moderate structured scenario testing. When transitioning to challenging unstructured environments, especially in GNSS-denied, texture-monotonous, and dense-vegetated natural fields, their performance can hardly sustain at a high level and requires further validation and improvement. To bridge this gap, we build a novel robot navigation dataset in a luxuriant botanic garden of more than 48000 m ² . Comprehensive sensors are used, including Gray and RGB stereo cameras, spinning and MEMS 3D LiDARs, and low-cost and industrial-grade IMUs, all of which are well calibrated and hardware-synchronized. An all-terrain wheeled robot is employed for data collection, traversing through thick woods, riversides, narrow trails, bridges, and grasslands, which are scarce in previous resources. This yields 33 short and long sequences, forming 17.1 km trajectories in total. Excitedly, both highly-accurate ego-motions and 3D map ground truth are provided, along with fine-annotated vision semantics. We firmly believe that our dataset can advance robot navigation and sensor fusion research to a higher level.

Railway inspection is an important task in railway maintenance to ensure safety. The fastener is a major part of the railway which fastens the tracks to the ground. The current article presents an efficient method to detect fasteners on the basis of image processing and pattern recognition techniques, which can be used to detect the absence of fasteners on the corresponding track in high-speed(up to 400 km/h). The Direction Field is extracted as the feature descriptor for recognition. In addition, the appropriate weight coefficient matrix is presented for robust and rapid matching in a complex environment. Experimental results are presented to show that the proposed method is computation efficient and robust for the detection of fasteners in a complex environment. Through the practical device fixed on the track inspection train, enough fastener samples are obtained, and the feasibility of the method is verified at 400 km/h.

This paper presents a distance accuracy technique for zero-offset industrial robot calibration based on 3D positioning and posture sensor (3DPP) along with modified Denavit–Hartenberg (MDH) for robot kinematics identification. Industrial robots distance and positioning accuracy tend to diminish rapidly due to repetitive tasks and heavy loads. Many aspects can be involved in reducing the distance and positioning accuracy of the industrial robot; therefore, using the proper sensors and identification methods is essential to increase, or restore the accuracy back to the original state. Normally, maintenance requires apparatus made specifically for robot kinematic calibration which are costly, time consuming and must be done by trained operators, while traditional calibration techniques suffer from cumulative error between the calibration device coordinates and robot coordinates. In this paper, a 3DPP measurement device and MDH robot kinematic identification method is proposed for a six degree of freedom (6DOF) industrial manipulator calibration, and a distance error calibration model is applied. The results indicate that the proposed the 3DPP sensor and calibration model based on MDH improved the absolute position accuracy and distance accuracy greatly, the positioning maximum and average error were reduced by 94% and 96% respectively, and the distance maximum and average error were reduced by 95% and 96% respectively. The experimental results confirm the efficiency of the sensor and proposed method.

The increasing train speed on railways generates an urgent need for more powerful automatic inspection of railway tracks, including real-time fastening component inspection. To obtain better high-speed performance with lower cost, this paper has proposed a novel structured light method based on motion image (SLMMI) for moving object inspection. The motion images in the proposed method are insensitive to motion, abundant with information, and easy to process, resulting in a low performance requirement of the hardware. Compared to the conventional unstructured light method and structured light method, the proposed method inherits the virtues of both thus offering a fresh perspective when inspecting missing fastening components on high-speed railways. By using the SLMMI and the recognition method based on a neural network, the experimental results yield good performance in terms of speed and accuracy. Furthermore, the robustness of the proposed method is also discussed and simulated by adding typical interferences, such as ambient light, vibration, and obstacles.

Directional dithering of a laser beam potentially limits the detection accuracy of a laser triangulation displacement probe. A theoretical analysis indicates that the measurement accuracy will linearly decrease as the laser dithering angle increases. To suppress laser dithering, a scheme for reduction of the influence of laser beam directional dithering in a laser triangulation displacement probe, which consists of a collimated red laser, a laser beam pointing control setup, a receiver lens, and a charge-coupled device, is proposed in this paper. The laser beam pointing control setup is inserted into the source laser beam and the measured object and can separate the source laser beam into two symmetrical laser beams. Hence, at the angle at which the source laser beam dithers, the positional averages of the two laser spots are equal and opposite. Moreover, a virtual linear function method is used to maintain a stable average of the positions of the two spots on the imaging side. Experimental results indicate that with laser beam pointing control, the estimated standard deviation of the fitting error decreases from 0.3531 $\mathrm{mm}$ to 0.0100 $\mathrm{mm}$ , the repeatability accuracy can be lowered from ±7 $\mathrm{mm}$ to ±5 $\mathsf{\mu }\mathrm{m}$ , and the nonlinear error can be reduced from ±6 % FS (full scale) to ±0.16 % FS.