Aiming at the problems of high cost and large size of the traditional mechanical lidar scanning point cloud, a point cloud data acquisition hardware system and a single-site cloud registration procedure were developed by using the prismatic lidar with low cost, small size and petal-shaped point cloud. Since the density of the point cloud collected by this lidar is time-dependent, in order to obtain a high-density point cloud, each station adopts a data collection method in which the motor-controlled lidar rotates 22.5 degrees each time, rotates 16 times, and scans the environment for one week. Using the self-developed station data processing programme, the data from each station were aligned according to the angle of the data by rotating the data through the space vector rotation algorithm. In the stage of inter-station point cloud registration, the original feature constraints of the multi-site cloud are obtained, and the error equations are derived from the constraints through the initial solution of all the station transformation parameters and unknown points except the control points. The weight function established by each constraint error is used as the constraint for iterative settlement until the iteration conditions are met, and all site space transformation parameters and location coordinates are output to achieve overall registration of multi-site cloud. This experiment shows that the point cloud data collected by the self-developed low-cost lidar has high density, high resolution, and the accuracy after registration is about 2cmin the nominal accuracy of prism lidar hardware, which has strong practicability and feasibility.
Most of the existing mobile LiDAR measurement systems adopt GNSS/INS combination method. This method requires GNSS signal to correct IMU positioning and attitude determination continuously. When GNSS signal is not received, IMU positioning and attitude determination accuracy rapidly increases from centimeter level to sub-meter level or even meter level. In order to solve the positioning defect of mobile measurement system when there is no GNSS signal, In this paper, a dual-mode mobile LiDAR measurement system integrating GNSS/INS positioning and INS/odometer positioning is proposed. It mainly judges whether there is GNSS signal input through the time synchronization controller and automatically switches between the two mode When there is GNSS signal, GNSS/INS positioning is used. When there is no GNSS signal, it automatically switches to INS/odometer positioning mode When there is no GNSS signal, The time synchronization controller simulate GNSS signal, collects its own high-precision quartz crystal oscillator to record time, and converts it into NEMA standard time signal and PPS signal for time synchronization of each sensor. The dual-mode mobile LiDAR measurement system can not only be used in highway measurement, but also solve the pose error in the case of GNSS unlocking such as subway and tunnel. It can be applied to point cloud measurement in a variety of scenes.
Road high precision mobile LiDAR measurement point cloud is a digital infrastructure in the fields of high precision map, automatic driving, High-precision automatic semantic segmentation of road point cloud is a key research direction at present. aiming at the problem that the semantic segmentation accuracy of existing deep learning networks is low for the uneven sparse point cloud measured by mobile LiDAR system, a deep learning method is proposed to divide point cloud data according to spatial location and considers the sampling point radius of regional groups. According to the spatial position of different objects, the method extracts the high-dimensional features of sampling points, and achieves the improvement of semantic segmentation accuracy of variable point cloud measured by high-speed mobile LiDAR system and carries out semantic segmentation experiment of The average test accuracy is 97.6%, and the mIOU reaches 0.82. The results show that compared with existing methods, the semantic results show that compared with the existing methods, the semantic segmentation accuracy of the proposed method is significantly improved for the uneven sparse road point cloud of mobile LiDAR system.
In response to the difficulties of processing massive laser point cloud off-site real-time scanning and on-site absolute coordinate system registration, a low-cost ground-based lidar measurement system is developed by combining Beidou/GNSS positioning system and 5G communication technology. The measurement system consists of LIDAR, a high-precision motor, BeiDou/GNSS receiver module and 5G module integration. Technology for real-time transmission. The terminal carries out multi-frame point cloud time registration by linear interpolation algorithm through self-developed data pre-processing software, coarse registration of multi-station laser point cloud according to BeiDou/GNSS coordinates, and then fine registration using Rodriguez matrix to complete the overall registration and visualization on the self-developed real-time point cloud management and visualization system. The experiments show that the measurement system can realize off-site scanning and transmission and provide data infrastructure for real-time application fields such as digital twins, monitoring of physical and cultural heritage, and analysis of construction and operation and maintenance of mega-shaped buildings.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.