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.
KEYWORDS: Clouds, Data modeling, 3D modeling, 3D metrology, Analytical research, 3D scanning, Laser scanners, Inspection, Feature extraction, Data acquisition
Building installation quality inspection is an active research field in the overlapping fields of engineering survey and civil engineering. Furthermore, ensuring the installation quality of temporary buildings in large-scale sports events has more practical and social significance. In this paper, Terrestrial Laser Scanner (TLS) is used to acquire the point cloud of the steel portal frame of the Zhangjiakou Olympic restaurant. Based on the established building coordinate datum, the data of the whole model is simplified, components are extracted and classified. This paper presents an automatic measurement method of verticality and deflection based on point cloud model. The results demonstrate that the mean error of verticality of the steel portal frame is ±0.002 m, and the mean error of member deflection is better than ±0.028 m. The whole structure installation quality meets the specification requirements. The terrestrial laser scanning (TLS) measurement meets temporary building installation detection accuracy requirements.
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.
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