This article introduced a positioning system composed of different sensors, such as LiDAR, IMU, and ultra-wideband (UWB), for the positioning method in autonomous driving technology under closed coal mine tunnels. First, we processed the LiDAR data, extracted its feature points and merged the extracted feature point clouds to generate a skewed combined feature point cloud. Then, we used the skew combined feature point clouds for feature matching, performed pre-integration processing on the IMU sensor data, and completed the LiDAR-IMU odometer with the LiDAR. Finally, we added UWB data to IMU pose node as a one-dimensional over-edge constraint. By updating the sliding window, the positioning accuracy was further improved. Moreover, we have conducted experiments to verify the proposed positioning system in a simulated roadway. The experimental results showed that the method proposed in this paper is superior to the single LiDAR method and the single UWB method in terms of positioning accuracy.

In order to make up for the shortcomings of independent sensors and provide more reliable estimation, a multi-sensor fusion framework for simultaneous localization and mapping is proposed in this paper. Firstly, the light detection and ranging (LiDAR) point cloud is screened in the front-end processing to eliminate abnormal points and improve the positioning and mapping accuracy. Secondly, for the problem of false detection when the LiDAR is surrounded by repeated structures, the intensity value of the laser point cloud is used as the screening condition to screen out robust visual features with high distance confidence, for the purpose of softening. Then, the initial factor, registration factor, inertial measurement units (IMU) factor and loop factor are inserted into the factor graph. A factor graph optimization algorithm based on a Bayesian tree is used for incremental optimization estimation to realize the data fusion. The algorithm was tested in campus and real road environments. The experimental results show that the proposed algorithm can realize state estimation and map construction with high accuracy and strong robustness.

Conveyor belts are key pieces of equipment for bulk material transport, and they are of great significance to ensure safe operation. With the development of belt conveyors in the direction of long distances, large volumes, high speeds, and high reliability, the use of inspection robots to perform full inspections of belt conveyors has not only improved the efficiency and scope of the inspections but has also eliminated the dependence of the traditional method on the density of sensor arrangement. In this paper, relying on the wireless-power-supply orbital inspection robot independently developed by the laboratory, aimed at the problem of the deviation of the belt conveyor, the methods for the diagnosis of the deviation of the conveyor belt and FPGA (field-programmable gate array) parallel computing technology are studied. Based on the traditional LSD (line segment detection) algorithm, a straight-line extraction IP core, suitable for an FPGA computing platform, was constructed. This new hardware linear detection algorithm improves the real-time performance and flexibility of the belt conveyor diagnosis mechanism.

LiDAR plays a pivotal role in the field of unmanned driving, but in actual use, it is often accompanied by errors caused by point cloud distortion, which affects the accuracy of various downstream tasks. In this paper, we first describe the feature of point cloud and propose a new feature point selection method Soft-NMS-Select; this method can obtain uniform feature point distribution and effectively improve the result of subsequent point cloud registration. Then, the point cloud registration is completed through the screened feature points, and the odometry information is obtained. For the motion distortion generated in a sweep, the prior information of the LiDAR’s own motion is obtained by using two linear interpolations, thereby improving the effect of motion compensation. Finally, for the distortion caused by the motion of objects in the scene, Euclidean clustering is used to obtain the position and normal vector of the center point of the point cloud cluster, and the motion pose of the object is calculated according to the offset between adjacent sweeps and eliminated distortion. Essentially, our method is a general point cloud compensation method that is applicable to all uses of LiDAR. This paper inserts this method into three SLAM algorithms to illustrate the effectiveness of the method proposed in this paper. The experimental results show that this method can significantly reduce the APE of the original SLAM algorithm and improve the mapping result.

The journal retracts the article titled “LiDAR-IMU-UWB-Based Collaborative Localization”