zuloopdf.blogg.se

1. #Missler angles of a triangle in curved space registration#
2. #Missler angles of a triangle in curved space verification#

Several methods for 3D modelling an indoor environment relying explicitly on the prior knowledge of scanner positions are in reference. The proposed method can process the large complex real-world point clouds from the unfiltered form into the separated room model, including suppressing of undesired objects inside a room. The paper deals with the automatic reconstruction of the fully volumetric 3D building models from oriented point clouds. Valuable work is presented by the authors in reference. The recommendations for this processing approach are stated. The results and algorithms are evaluated also in terms of precision against the physical space dimensions and comparison with the convex hull results. In the results part, the processing of the complex point cloud with several rooms is presented.

#Missler angles of a triangle in curved space verification#

The paper further includes the description of our developed point cloud processing pipeline and the presentation of the new methods in detail to give a comprehensive overview of our developed approach, including verification on real space data. In the Introduction section is a survey about different point cloud processing methods for basic object detection and segmentation, the planar surface and space features estimation, and their typical application. This paper is focused on the presentation of several new methods for point cloud processing such as the outlier points removal, estimation of the initial point cloud rotation, point cloud data correction and recovery, and the vertices detection of a planar surface. Moreover, the presented approach is usable not only for point clouds but also as the general processing and analysis of different levels for any 3D data. It also serves to other researchers as the extension to the used methods presented in the following subsection. This connection allows: obtaining important space features such as the area, perimeter and volume the space and statistical descriptions of planar surfaces, including the descriptive point amount decreasing by the vectorization of vertices the correction and recovery of missing space data and a planar surface presented as the image allows the alternative way of the physical points storage, including its visualization. The main advantages of our approach lie in the combination of the physical space points quantization with image processing methods. Our contribution in point cloud processing is to show a different alternative way of processing. We rather look for vertices of the analyzed planar surface. For example, individual points of a planar surface composed of a thousand points are not so important for the subsequent processing. This will allow the decreased memory consumption of physical data storage. The main issue of our research is point cloud processing and simplification with the focus on the detection, statistical description, vectorization, and visualization of basic space features. This proposed approach is verified on the real indoor point clouds. More importantly, planar surface detection shows a 99% decrease in necessary descriptive points almost in all cases. The results show the reliability of our approach to detect close parallel walls with suitable parameter settings. All of these processes are achieved by applying advanced image processing methods in combination with the quantization of physical space points. We introduce our approach to preprocess an input point cloud in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features. This paper describes a novel method to reduce the burden of processing for multiple point cloud scans.

This method requires further processing and places high demands on memory consumption, especially for small embedded devices in mobile robots.

#Missler angles of a triangle in curved space registration#

However, a single point cloud scan does not cover the whole area, so multiple point cloud scans must be acquired and compared together to find the right matching between them in a process called registration method.

Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud.