The sensing system consisting of Light Detection and Ranging (LiDAR) and a camera provides complementary information about the surrounding environment. To take full advantage of multi-source data provided by different sensors, an accurate fusion of multi-source sensor information is needed. Time synchronization and space registration are the key technologies that affect the fusion accuracy of multi-source sensors. Due to the difference in data acquisition frequency and deviation in startup time between LiDAR and the camera, asynchronous data acquisition between LiDAR and camera is easy to occur, which has a significant influence on subsequent data fusion. Therefore, a time synchronization method of multi-source sensors based on frequency self-matching is developed in this paper. Without changing the sensor frequency, the sensor data are processed to obtain the same number of data frames and set the same ID number, so that the LiDAR and camera data correspond one by one. Finally, data frames are merged into new data packets to realize time synchronization between LiDAR and camera. Based on time synchronization, to achieve spatial synchronization, a nonlinear optimization algorithm of joint calibration parameters is used, which can effectively reduce the reprojection error in the process of sensor spatial registration. The accuracy of the proposed time synchronization method is 99.86% and the space registration accuracy is 99.79%, which is better than the calibration method of the Matlab calibration toolbox.

Light Detection and Ranging (LiDAR) is a crucial roadside intelligent perception device in Cooperative Vehicle Infrastructure System, which can generate a large amount of disordered 3D point cloud data. Point cloud clustering serves as a prerequisite for road target identification, trajectory tracking, and traffic conflict prediction. However, due to limitations in data collection methods and clustering algorithms, a pronounced delay in target clustering exists. In this work, a real-time point cloud clustering algorithm for roadside LiDAR (RTPCC-RL) is proposed, which primarily comprises three aspects: online point cloud capture, background point cloud filtering, and real-time clustering of target point clouds, implemented using a C++ program. Initially, point cloud information can be captured and extracted by decoding User Datagram Protocol (UDP) packets. Upon receiving a UDP data packet, background filtering is achieved through the point distance difference method, reducing computational complexity and enhancing processing speed. Furthermore, the point cloud information of the background frame is stored in various matrices based on different vertical angles for efficient point location. Real-time clustering of target point clouds is executed based on voxel data features. The spatial region of the lane can be divided into voxel grids, and voxel point cloud coordinates are sequentially organized. The proposed algorithm was compared with other methods and the experimental results with a 32-channel LiDAR demonstrated that the RTPCC-RL effectively clustered road vehicle cloud points online with high precision and recall rates, achieving a processing time of 100 ms. The code can be found on the website https://github.com/480196239xiaoman/LiDAR/blob/main/online_cluster.cpp.

For the current problem of cycle slips in the observation data of the BDS-2 and BDS-3 (Bei Dou Navigation Satellite System), in this paper, BDS B1I and B3I signals are used as research objects to study the detection of cycle slips, and their repair algorithm. The Geometry-free (GF) and Melbourne–Wübeena (MW) combination algorithm are used for the detection of cycle slips. A new method of arc partition is proposed in this work to detect cycle slips as the boundary to delimit two different observation arcs. In this way, the different values of cycle slips can be divided and marked. Moreover, the gross errors can be removed. Finally, the detection of cycle slips and the analysis of all epochs can be completed and repaired. This work also analyzes the dual-frequency data effect of cycle slips on code multipath observation. The results showed that this method greatly improved the speed of detection of cycle slips.

With the increasing number of vehicles and the continuous increase of highway mileage. Traffic safety issues are gradually coming into view. Placing lidar on the side of the road can increase the safety of driving at high highway speeds. In order to achieve uninterrupted detection of vehicles on the highway. It is necessary to rationally deploy lidar. In this paper, the installation inclination, installation height, installation distance from the highway and the layout distance of adjacent lidar are studied. And the optimal layout scheme is given according to the highway in different scenarios.