Abstract
Abstract. In this paper, a method is proposed for solving relative translations of 3D point clouds collected by Mobile Laser Scanning (MLS) techniques. The proposed approach uses the attributes of the 3D points to generate and match 2D-projections, by employing a simple correlation technique instead of matching in 3D. As a result, the developed method depends more on the number of pixels in the 2D-projections and less on the number of points in the point clouds. This leads to a more cost-efficient method in contrast to 3D registration techniques. The method uses this benefit to provide redundant translation parameters for each point cloud pair. With the utilization of image-based evaluation criteria the reliable translation parameters are detected and only those are used to compute the final solution. Consequently, the confidence levels of each final estimation can be computed. In addition, an indication of robustness showing how many estimations where included for the computation of the final solution is included. It is shown that the method performs fast due to its simplicity especially when medium image resolution’s such as 0.15 m are used. Reliable matches can be produced even when the overlap of the point cloud sets is small or the initial offset large as long as the offsets are distinguishable in the projections. Furthermore, a technique is proposed to obtain capabilities for sub-pixel accuracy estimations, as the accuracy of the estimations is restricted to the grid cell size. The technique seems promising, but further improvement is necessary.
Highlights
Introduction1.1 Problem statementPoint cloud data is an important source of 3D spatial information, as vast amounts of highly dense 3D points can be collected with laser scanning techniques in a considerably short amount of time
Point cloud data is an important source of 3D spatial information, as vast amounts of highly dense 3D points can be collected with laser scanning techniques in a considerably short amount of time
The standard deviations of the results are not always small showing that the created imagery from the point clouds could be improved to produce more robust registrations
Summary
1.1 Problem statementPoint cloud data is an important source of 3D spatial information, as vast amounts of highly dense 3D points can be collected with laser scanning techniques in a considerably short amount of time. During a MLS process it is usually necessary to record a certain scene more than once to retrieve a complete representation of it, for instance at a road junction. In such cases, point clouds representing (part of) the same scene but retrieved at different epochs do not perfectly match. When signal multipathing or blockage occurs the navigation solution has poor quality or can even be unavailable The positioning in such cases depends on an Inertial Measurement Unit (IMU), which calculates positions based on displacements from an initial known position (Levi and Judd, 1996). This leads to the accumulation of potential encountered positioning errors
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