This group of Chinese, U.S. and Canadian researchers are proposing an innovative mathematical method to improve the registration accuracy of long strip point clouds such as would be found in a tunnel or rail survey.
From the paper:
When applied to elongated features such as high-speed rails, TLS presents challenges as such applications require numerous long-strip scans to complete a scene. The point cloud sets acquired by such successive scans are referenced to different local frames, each associated with a corresponding scanner location. Therefore, a registration process is needed to align and merge these individual scans relative to a common reference frame. This registration results in the generation of long 3D strips, like the long geolocated 2D mosaics derived through image co-registration [6]. The co-registration of individual point cloud scenes is a prototypical photogrammetric problem, comparable to traditional strip adjustments, and requiring the estimation of transformation parameters describing the relative position of two overlapping 3D models, namely the scale, shifts, and rotation of one 3D scene relative to another. This is the subject of this publication.
The registered TLS data are the final measurement product and are in the form of a point cloud, consisting of a set of data points defined by their X, Y, and Z coordinates. However, these final measurement products are normally distorted due to registration errors and these errors are accumulated in multi-site cloud registration, especially in surveyed areas with limited control points. The resulting compromised accuracy not only affects the reliability but also restricts the applications of TLS data.
Overall, the accuracy of the acquired TLS data is affected by two primary aspects: ranging accuracy of the scanner, and registration accuracy among multiple point cloud data [7,8]. As a simple and robust method for finding a set of inliers, the popular RANdom Sample Consensus (RANSAC) algorithm (Fischler and Bolles [9]), can be used to register point clouds. However, the accuracy of RANSAC is affected by determining the assumed noise in the surface direction, a function of the distance from an object to the laser scanner, incident angle, surface texture, and point clouds generated by multiple scan setups. In order to improve the overall accuracy, we propose a RANSAC-based registration that is enhanced through a Closed Constraint Adjustment (CCA). This Closed Constraint Adjustment (CCA) ensures that loops are closed. In the context of this publication we consider as representative objects to be measured the types of bridge piers that are used as basic docks for high speed rail (HSR).
Sounds promising.
