scholarly journals LINE SEGMENTATION OF 2D LASER SCANNER POINT CLOUDS FOR INDOOR SLAM BASED ON A RANGE OF RESIDUALS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 363-369 ◽  
Author(s):  
M. Peter ◽  
S. R. U. N. Jafri ◽  
G. Vosselman
Keyword(s):  
Laser Scanning ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Laser Scanner ◽  
Point Clouds ◽  
Indoor Environments ◽  
Six Degrees Of Freedom ◽  
Localization And Mapping ◽  
Feature Based ◽  
Definition Of

Indoor mobile laser scanning (IMLS) based on the Simultaneous Localization and Mapping (SLAM) principle proves to be the preferred method to acquire data of indoor environments at a large scale. In previous work, we proposed a backpack IMLS system containing three 2D laser scanners and an according SLAM approach. The feature-based SLAM approach solves all six degrees of freedom simultaneously and builds on the association of lines to planes. Because of the iterative character of the SLAM process, the quality and reliability of the segmentation of linear segments in the scanlines plays a crucial role in the quality of the derived poses and consequently the point clouds. The orientations of the lines resulting from the segmentation can be influenced negatively by narrow objects which are nearly coplanar with walls (like e.g. doors) which will cause the line to be tilted if those objects are not detected as separate segments. State-of-the-art methods from the robotics domain like Iterative End Point Fit and Line Tracking were found to not handle such situations well. Thus, we describe a novel segmentation method based on the comparison of a range of residuals to a range of thresholds. For the definition of the thresholds we employ the fact that the expected value for the average of residuals of <i>n</i> points with respect to the line is <i>σ</i>&amp;thinsp;/&amp;thinsp;&amp;radic;<i>n</i>. Our method, as shown by the experiments and the comparison to other methods, is able to deliver more accurate results than the two approaches it was tested against.

scholarly journals Efficient RGB–D data processing for feature–based self–localization of mobile robots

2016 ◽  
Vol 26 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Marek Kraft ◽  
Michał Nowicki ◽  
Rudi Penne ◽  
Adam Schmidt ◽  
Piotr Skrzypczyński
Keyword(s):  
Feature Extraction ◽  
Degrees Of Freedom ◽  
Active Vision ◽  
Visual Odometry ◽  
Data Sets ◽  
Six Degrees Of Freedom ◽  
Localization And Mapping ◽  
Feature Based ◽  
General Architecture ◽  
Mapping Solution

Abstract The problem of position and orientation estimation for an active vision sensor that moves with respect to the full six degrees of freedom is considered. The proposed approach is based on point features extracted from RGB-D data. This work focuses on efficient point feature extraction algorithms and on methods for the management of a set of features in a single RGB-D data frame. While the fast, RGB-D-based visual odometry system described in this paper builds upon our previous results as to the general architecture, the important novel elements introduced here are aimed at improving the precision and robustness of the motion estimate computed from the matching point features of two RGB-D frames. Moreover, we demonstrate that the visual odometry system can serve as the front-end for a pose-based simultaneous localization and mapping solution. The proposed solutions are tested on publicly available data sets to ensure that the results are scientifically verifiable. The experimental results demonstrate gains due to the improved feature extraction and management mechanisms, whereas the performance of the whole navigation system compares favorably to results known from the literature.

scholarly journals FIRST EXPERIENCES WITH THE TRIMBLE SX10 SCANNING TOTAL STATION FOR BUILDING FACADE SURVEY

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 405-412 ◽  
Author(s):  
E. Lachat ◽  
T. Landes ◽  
P. Grussenmeyer
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Laser Scanner ◽  
Point Clouds ◽  
User Needs ◽  
Research Areas ◽  
Points Of Interest ◽  
Building Facade ◽  
Technological Advances ◽  
Aerial Vehicle

The use of Terrestrial Laser Scanner (TLS) tends to become a solution in many research areas related to large scale surveying. Meanwhile, the technological advances combined with the investigation of user needs have brought to the design of innovative devices known as scanning total stations. Such instruments merge in a unique hardware both scanning and surveying facilities. Even if their scanning rate is often reduced compared to conventional TLS, they make it possible to directly georeference laser scanning projects and to complete them with measurements of individual points of interest. The recent Trimble SX10 which was launched on the market in early October 2016 has been tested and some experiences carried out with it are reported in this paper. The analyses mainly focus on the survey of a building facade. Next to laser scanning survey, a photogrammetry campaign using an Unmanned Aerial Vehicle (UAV) has been carried out. These different datasets are used to assess the Trimble SX10 issued point clouds through a set of comparisons. Since georeferencing is possible either directly or indirectly using this device, data processed both ways are also compared to conclude about the more reliable method.

scholarly journals ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

2020 ◽  
Vol V-5-2020 ◽  
pp. 117-123
Author(s):  
C. Wang ◽  
Y. Dai ◽  
N. Elsheimy ◽  
C. Wen ◽  
G. Retscher ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Point Clouds ◽  
Indoor Positioning ◽  
Benchmark Dataset ◽  
Scanning System ◽  
Indoor Environments ◽  
Process Data ◽  
Localization And Mapping ◽  
Common Framework

Abstract. In this paper, we present a publicly available benchmark dataset on multisensorial indoor mapping and positioning (MiMAP), which is sponsored by ISPRS scientific initiatives. The benchmark dataset includes point clouds captured by an indoor mobile laser scanning system in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) LiDAR-based Simultaneous Localization and Mapping (SLAM); (2) automated Building Information Model (BIM) feature extraction; and (3) multisensory indoor positioning. The MiMAP project provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone-based indoor positioning methods. This paper describes the multisensory setup, data acquisition process, data description, challenges, and evaluation metrics included in the MiMAP project.

scholarly journals AUTOMATIC EXTRACTION OF A NAVIGATION GRAPH INTENDED FOR INDOORGML FROM AN INDOOR POINT CLOUD

2019 ◽  
Vol IV-2/W5 ◽  
pp. 271-278
Author(s):  
P. Flikweert ◽  
R. Peters ◽  
L. Díaz-Vilariño ◽  
R. Voûte ◽  
B. Staats
Keyword(s):  
Laser Scanning ◽  
Medial Axis ◽  
Laser Scanner ◽  
Point Clouds ◽  
Essential Elements ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Architectural Drawings ◽  
The Public ◽  
First Results

<p><strong>Abstract.</strong> Indoor environments tend to be more complex and more populated when buildings are accessible to the public. The need for knowing where people are, how they can get somewhere or how to reach them in these buildings is thus equally increasing. In this research point clouds are used, obtained by dynamic laser scanning of a building, since we cannot rely on architectural drawings for maps and paths, which can be outdated. The presented method focuses on the creation of an indoor navigation graph, based on IndoorGML structure, in a fast and automated way, while retaining the type of walkable surface. In this paper the focus has been on door detection, because doors are essential elements in an indoor environment, seeing that they connect spaces and are a logical step in a route. This paper describes a way to detect doors using 3D Medial Axis Transform (MAT) combined with the intelligence stored in the path of a mobile laser scanner, showing good first results. Additionally different spaces (e.g. rooms and corridors) in the building are identified and slopes and stairs in walkable spaces are detected. This results in a navigation graph which can be stored in an IndoorGML structure.</p>

scholarly journals The Combined Use of SLAM Laser Scanning and TLS for the 3D Indoor Mapping

Buildings ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 386
Author(s):  
Aino Keitaanniemi ◽  
Juho-Pekka Virtanen ◽  
Petri Rönnholm ◽  
Antero Kukko ◽  
Toni Rantanen ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Information Modeling ◽  
Mapping Strategy ◽  
Localization And Mapping ◽  
Combined Use ◽  
Building Information

An efficient 3D survey of a complex indoor environment remains a challenging task, especially if the accuracy requirements for the geometric data are high for instance in building information modeling (BIM) or construction. The registration of non-overlapping terrestrial laser scanning (TLS) point clouds is laborious. We propose a novel indoor mapping strategy that uses a simultaneous localization and mapping (SLAM) laser scanner (LS) to support the building-scale registration of non-overlapping TLS point clouds in order to reconstruct comprehensive building floor/3D maps. This strategy improves efficiency since it allows georeferenced TLS data to only be collected from those parts of the building that require such accuracy. The rest of the building is measured with SLAM LS accuracy. Based on the results of the case study, the introduced method can locate non-overlapping TLS point clouds with an accuracy of 18–51 mm using target sphere comparison.

scholarly journals SEE-THROUGH IMAGING OF LASER-SCANNED 3D CULTURAL HERITAGE OBJECTS BASED ON STOCHASTIC RENDERING OF LARGE-SCALE POINT CLOUDS

2016 ◽  
Vol III-5 ◽  
pp. 73-80 ◽  
Author(s):  
S. Tanaka ◽  
K. Hasegawa ◽  
N. Okamoto ◽  
R. Umegaki ◽  
S. Wang ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Laser Scanning ◽  
Large Scale ◽  
Laser Scanner ◽  
Point Clouds ◽  
Cultural Value ◽  
Stochastic Algorithm ◽  
Scale Point ◽  
3D Scene ◽  
Cultural Heritage Objects

We propose a method for the precise 3D see-through imaging, or transparent visualization, of the large-scale and complex point clouds acquired via the laser scanning of 3D cultural heritage objects. Our method is based on a stochastic algorithm and directly uses the 3D points, which are acquired using a laser scanner, as the rendering primitives. This method achieves the correct depth feel without requiring depth sorting of the rendering primitives along the line of sight. Eliminating this need allows us to avoid long computation times when creating natural and precise 3D see-through views of laser-scanned cultural heritage objects. The opacity of each laser-scanned object is also flexibly controllable. For a laser-scanned point cloud consisting of more than 10&lt;sup&gt;7&lt;/sup&gt; or 10&lt;sup&gt;8&lt;/sup&gt; 3D points, the pre-processing requires only a few minutes, and the rendering can be executed at interactive frame rates. Our method enables the creation of cumulative 3D see-through images of time-series laser-scanned data. It also offers the possibility of fused visualization for observing a laser-scanned object behind a transparent high-quality photographic image placed in the 3D scene. We demonstrate the effectiveness of our method by applying it to festival floats of high cultural value. These festival floats have complex outer and inner 3D structures and are suitable for see-through imaging.

scholarly journals SEE-THROUGH IMAGING OF LASER-SCANNED 3D CULTURAL HERITAGE OBJECTS BASED ON STOCHASTIC RENDERING OF LARGE-SCALE POINT CLOUDS

2016 ◽  
Vol III-5 ◽  
pp. 73-80 ◽  
Author(s):  
S. Tanaka ◽  
K. Hasegawa ◽  
N. Okamoto ◽  
R. Umegaki ◽  
S. Wang ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Laser Scanning ◽  
Large Scale ◽  
Laser Scanner ◽  
Point Clouds ◽  
Cultural Value ◽  
Stochastic Algorithm ◽  
Scale Point ◽  
3D Scene ◽  
Cultural Heritage Objects

We propose a method for the precise 3D see-through imaging, or transparent visualization, of the large-scale and complex point clouds acquired via the laser scanning of 3D cultural heritage objects. Our method is based on a stochastic algorithm and directly uses the 3D points, which are acquired using a laser scanner, as the rendering primitives. This method achieves the correct depth feel without requiring depth sorting of the rendering primitives along the line of sight. Eliminating this need allows us to avoid long computation times when creating natural and precise 3D see-through views of laser-scanned cultural heritage objects. The opacity of each laser-scanned object is also flexibly controllable. For a laser-scanned point cloud consisting of more than 10<sup>7</sup> or 10<sup>8</sup> 3D points, the pre-processing requires only a few minutes, and the rendering can be executed at interactive frame rates. Our method enables the creation of cumulative 3D see-through images of time-series laser-scanned data. It also offers the possibility of fused visualization for observing a laser-scanned object behind a transparent high-quality photographic image placed in the 3D scene. We demonstrate the effectiveness of our method by applying it to festival floats of high cultural value. These festival floats have complex outer and inner 3D structures and are suitable for see-through imaging.

scholarly journals BAROQUE BANDED VAULTS: SURVEYING AND MODELING. THE CASE STUDY OF A NOBLE PALACE IN TURIN

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 871-878
Author(s):  
M. C. López González ◽  
R. Spallone ◽  
M. Vitali ◽  
F. Natta
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Architectural Drawing ◽  
Three Dimensional Modeling ◽  
Use Of Data ◽  
Dimensional Modeling ◽  
Set Up ◽  
Definition Of

Abstract. This paper presents the methodological framework set up for the analysis, interpretation, and representation of the banded vaulted systems recognized in eleven Baroque atria in Turin. In these atria, the banded vaults, locally named “a fascie”, are featured by a series of arches orthogonal to the perimeter walls on which they rest. The arches divide the room’s ceiling into spaces that can accommodate small vaults of different shapes. The atria have been the subject of bibliographical, historical and documentary analyses, laser scanner metric survey, two-dimensional graphic representations, and interpretative hypotheses through three-dimensional modeling of the design’s geometries of the vaults.The integration between terrestrial laser scanning (TLS) technique, architectural drawing and three-dimensional modeling methods led to the definition of new workflows, aimed at optimizing the use of data. From these procedures new opportunities for the research arise, such as the comparison (metric and geometric) through the superimposition of design ideal models and point clouds.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

scholarly journals Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

2019 ◽  
Vol 11 (12) ◽  
pp. 1453 ◽  
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Lili Lin ◽  
Chenglu Wen ◽  
Chenhui Yang ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Laser Scanning ◽  
Large Scale ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Point Clouds ◽  
Traffic Sign ◽  
Traffic Signs ◽  
3D Point Clouds ◽  
Traffic Environment

Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.

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