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Recovering Missing Trajectory From Point Cloud

grpahic for Recovering Trajectory for MLS Surveys

Recovering Trajectory for MLS Surveys

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. Recovering that data from the point cloud can be useful in certain situtations where that may be the only option.

Significant progress has been made in research on reconstructing scene models, extracting typical objects, and road surveys [11] based on MLS data. However, 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.

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.

For the complete paper click here.

You may also want to review this paper by Che and Olsen that provides even better results.

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