Multiple TLS Point Cloud Registration Based on Point Projection Images

Abstract

ABSTRACT:In this paper, an efficient and robust registration method of multiple point clouds is proposed. In our research, we assume that point clouds are acquired by Terrestrial Laser Scanning (TLS) systems, and the scanned environments have a relatively flat base plane such as the ground or a floor. Our method is based on an existing pairwise registration method based on point projection images, which can quickly register the point clouds under the above assumptions. In the method, sliced point clouds are projected onto the base plane, and a binary image with feature points is created. The registration is done by using feature points of the images based on the sample consensus strategy. In this paper, first, we improve the efficiency of the pairwise registration method by introducing height and occlusion information to the image. Then, a validity check method of pairwise registration using space-classified images is proposed to avoid exhaustive pairwise registration in the multiple point cloud registration process. Finally, an efficient multiple point cloud registration algorithm based on progressive creation of a point cloud connectivity graph using iterative rough and precise pairwise registration and the validity check method is proposed. The effectiveness of our method is shown through its application to three datasets of outdoor environments.

Modelo para evaluación de la exactitud de escáner láser terrestre - TLS

“…En la búsqueda de un modelo estadístico, que explique el comportamiento de las observaciones obtenidas en campo de los targets levantados por medio de la topografía convencional y el TLS (Poreba & Goulette, 2015), se acudió a diferentes áreas de la estadística, entre las cuales se encuentran la geoestadística y los modelos lineales generalizados (MLG), siendo esta última la solución óptima para el fin del estudio. Por un lado, la geoestadística prometía ser una herramienta útil en la predicción del error obtenido al realizar las mediciones con el escáner láser, puesto que considera la correlación espacial que pueda existir entre las observaciones en un mismo escaneo; sin embargo, dichas predicciones tenían un horizonte de tan solo 20 metros y estaban limitadas al espacio donde se realizaron las medidas, el cual es bastante ideal; contrario al sinnúmero de configuraciones que pueden encontrarse en la práctica (Sumi, Date & Kanai, 2018).…”

Fast Registration of Terrestrial LiDAR Point Clouds Based on Gaussian-Weighting Projected Image Matching

“…Triangles are constructed in [31] based on the intersection points of extracted vertical lines to match point clouds, which is unstable for scenes with few vertical linear features. [32] uses Douglas-Peucker for contour detection to match point clouds. Since this method does not consider feature descriptor, the matching process could be rather time-consuming.…”

Mixed Reality Visualization of Point Clouds for Supporting Terrestrial Laser Scanning

“…Therefore, pre-computation should be avoided as much as possible, and short computation time should be desirable. To meet these demands, in our system, a global localization method using simple and efficient rough registration for TLS point clouds (Sumi et al, 2018) has been developed and evaluated in a real-world scene.…”