Recovering Missing Trajectory Data for Mobile Laser Scanning Systems

Mianqing Zhong,Chuanshuai Zhang,Zhihua Wang,Lichun Sui,Nan Chen,Xiaomei Yang

doi:10.3390/rs12060899

Abstract

Trajectory data are often used as important auxiliary information in preprocessing and extracting the target from mobile laser scanning data. However, the trajectory data stored independently may be lost and destroyed for various reasons, making the data unavailable for the relevant models. This study proposes recovering the trajectory of the scanner from point cloud data following the scanning principles of a rotating mirror. Two approaches are proposed from different input conditions: Ordered three-dimensional coordinates of point cloud data, with and without acquisition time. We recovered the scanner’s ground track through road point density analysis and restored the position of the center of emission of the laser based on plane reconstruction on a single scanning line. The validity and reliability of the proposed approaches were verified in the four typical urban, rural, winding, and viaduct road environments using two systems from different manufacturers. The result deviations of the ground track and scanner trajectory from their actual position were a few centimeters and less than 1 decimeter, respectively. Such an error is sufficiently small for the trajectory data to be used in the relevant algorithms.

Highlights

Mobile laser scanning (MLS) systems collect a large number of three-dimensional (3D) road information along a vehicle’s trajectory with high precision [1], and have been widely applied to base surveying [2], road and traffic engineering [3,4,5,6], urban planning and management [7], digital cities [8], forestry investigation [9], and cultural relics’ protection [10]
By focusing on certain cases where the trajectory data are unavailable, this study proposed recovering the scanner’s trajectory from MLS point data based on two general input conditions
Rough ground tracks of the scanner were located by the point density of the road and refined based on an isochromatic sequence of acquisition times or a line smoothing operation

Summary

Introduction

Mobile laser scanning (MLS) systems collect a large number of three-dimensional (3D) road information along a vehicle’s trajectory with high precision [1], and have been widely applied to base surveying [2], road and traffic engineering [3,4,5,6], urban planning and management [7], digital cities [8], forestry investigation [9], and cultural relics’ protection [10]. Significant progress has been made in research on reconstructing scene models, extracting typical objects, and road surveys [11] based on MLS data. It remains a challenging task because of the degraded positioning accuracy at urban canyons and city centers, the large amount of data collected, the complexity of the road environment, and occlusion. Providing the position information of the sensor, vehicular trajectory is often used as important auxiliary information to process the large amounts of MLS data [12]. Wu et al [23]

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Conclusion