scholarly journals Spatial change detection using normal distributions transform

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Ukyo Katsura ◽  
Kohei Matsumoto ◽  
Akihiro Kawamura ◽  
Tomohide Ishigami ◽  
Tsukasa Okada ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Change Detection ◽  
Laser Scanner ◽  
Point Clouds ◽  
Measured Data ◽  
Spatial Change ◽  
Grid Data ◽  
Point Distribution ◽  
Normal Distributions ◽  
Geometrical Information

AbstractSpatial change detection is a fundamental technique for finding the differences between two or more pieces of geometrical information. This technique is critical in some robotic applications, such as search and rescue, security, and surveillance. In these applications, it is desirable to find the differences quickly and robustly. The present paper proposes a fast and robust spatial change detection technique for a mobile robot using an on-board range sensors and a highly precise 3D map created by a 3D laser scanner. This technique first converts point clouds in a map and measured data to grid data (ND voxels) using normal distributions transform. The voxels in the map and the measured data are then compared according to the features of the ND voxels. Three techniques are introduced to make the proposed system robust for noise, that is, classification of point distribution, overlapping of voxels, and voting using consecutive sensing. The present paper shows the results of indoor and outdoor experiments using an RGB-D camera and an omni-directional laser scanner mounted on a mobile robot to confirm the performance of the proposed technique.

scholarly journals Terrestrial laser scanning in monitoring of anthropogenic objects

2017 ◽  
Vol 66 (2) ◽  
pp. 347-364
Author(s):  
Janina Zaczek-Peplinska ◽  
Maria Kowalska
Keyword(s):  
Laser Scanning ◽  
Incidence Angle ◽  
Geometric Analysis ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Angle Of Incidence ◽  
Point Distribution ◽  
Common Coordinate System ◽  
Cloud Of Points

Abstract The registered xyz coordinates in the form of a point cloud captured by terrestrial laser scanner and the intensity values (I) assigned to them make it possible to perform geometric and spectral analyses. Comparison of point clouds registered in different time periods requires conversion of the data to a common coordinate system and proper data selection is necessary. Factors like point distribution dependant on the distance between the scanner and the surveyed surface, angle of incidence, tasked scan’s density and intensity value have to be taken into consideration. A prerequisite for running a correct analysis of the obtained point clouds registered during periodic measurements using a laser scanner is the ability to determine the quality and accuracy of the analysed data. The article presents a concept of spectral data adjustment based on geometric analysis of a surface as well as examples of geometric analyses integrating geometric and physical data in one cloud of points: cloud point coordinates, recorded intensity values, and thermal images of an object. The experiments described here show multiple possibilities of usage of terrestrial laser scanning data and display the necessity of using multi-aspect and multi-source analyses in anthropogenic object monitoring. The article presents examples of multisource data analyses with regard to Intensity value correction due to the beam’s incidence angle. The measurements were performed using a Leica Nova MS50 scanning total station, Z+F Imager 5010 scanner and the integrated Z+F T-Cam thermal camera.

scholarly journals Robust Spatial Approximation of Laser Scanner Point Clouds by Means of Free-form Curve Approaches in Deformation Analysis

2016 ◽  
Vol 10 (1) ◽  
Author(s):  
Johannes Bureick ◽  
Hamza Alkhatib ◽  
Ingo Neumann
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Measured Data ◽  
Free Form ◽  
Deformation Analysis ◽  
B Spline ◽  
B Splines ◽  
Spline Fitting ◽  
Data Gaps ◽  
Spatial Approximation

AbstractIn many geodetic engineering applications it is necessary to solve the problem of describing a measured data point cloud, measured, e. g. by laser scanner, by means of free-form curves or surfaces, e. g., with B-Splines as basis functions. The state of the art approaches to determine B-Splines yields results which are seriously manipulated by the occurrence of data gaps and outliers.Optimal and robust B-Spline fitting depend, however, on optimal selection of the knot vector. Hence we combine in our approach Monte-Carlo methods and the location and curvature of the measured data in order to determine the knot vector of the B-Spline in such a way that no oscillating effects at the edges of data gaps occur. We introduce an optimized approach based on computed weights by means of resampling techniques. In order to minimize the effect of outliers, we apply robust M-estimators for the estimation of control points.The above mentioned approach will be applied to a multi-sensor system based on kinematic terrestrial laserscanning in the field of rail track inspection.

Spatial change detection using voxel classification by normal distributions transform

2019 ◽  
Author(s):  
Ukyo Katsura ◽  
Kohei Matsumoto ◽  
Akihiro Kawamura ◽  
Tomohide Ishigami ◽  
Tsukasa Okada ◽  
...  
Keyword(s):  
Change Detection ◽  
Spatial Change ◽  
Normal Distributions ◽  
Voxel Classification

scholarly journals LiDAR-based Simultaneous Localization and Mapping in an underground mine in Złoty Stok, Poland

2021 ◽  
Vol 942 (1) ◽  
pp. 012035
Author(s):  
P Trybała
Keyword(s):  
Mobile Robot ◽  
Spatial Data ◽  
Laser Scanner ◽  
Simultaneous Localization And Mapping ◽  
Point Clouds ◽  
Working Environment ◽  
Accuracy Evaluation ◽  
Process Data ◽  
3D Point Clouds ◽  
Localization And Mapping

Abstract The mining sector is one of the most promising areas for implementing advanced autonomous robots. The benefits of increased safety, robot actions’ repeatability, and reducing human presence in hazardous locations are especially important in underground mines. One of the core functionalities of such a device is the robot’s ability to localize and navigate itself in the working environment. To achieve this, simultaneous localization and mapping (SLAM) techniques are used. In selected cases, they also allow the acquisition of dense spatial data in the form of 3D point clouds, which can be utilized for various 3D modeling and spatial analysis purposes. In this work, a mobile robot, equipped only with a compact laser scanner, is used to acquire spatial data in the adit of a closed mine in Zloty Stok, Poland. This data is further processed with selected SLAM algorithms to create a homogeneous 3D point cloud. Results are visualized and compared to a model obtained with a survey-grade laser scanner. Accuracy evaluation shows that employing SLAM algorithms to process data collected by a mobile robot can produce a reasonably accurate 3D geometrical model of an underground tunnel, even without incorporating any additional sensors.

High-speed spatial change detection using point clouds and NDT

2019 ◽  
Vol 2019 (0) ◽  
pp. 2A1-R08
Author(s):  
Ukyou KATSURA ◽  
Kohei MATSUMOTO ◽  
Akihiro KAWAMURA ◽  
Ryo KURAZUME ◽  
Tomohide ISHIGAMI ◽  
...  
Keyword(s):  
Change Detection ◽  
High Speed ◽  
Point Clouds ◽  
Spatial Change

scholarly journals Control of Laser Scanner Trilateration Networks for Accurate Georeferencing of Caves: Application to El Castillo Cave (Spain)

2021 ◽  
Vol 13 (24) ◽  
pp. 13526
Author(s):  
Vicente Bayarri ◽  
Elena Castillo ◽  
Sergio Ripoll ◽  
Miguel A. Sebastián
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Measured Data ◽  
Field Of View ◽  
Terrestrial Laser Scanner ◽  
Multiple Points ◽  
Different Types ◽  
Points Of View ◽  
Made In

There is a growing demand for measurements of natural and built elements, which require quantifiable accuracy and reliability, within various fields of application. Measurements from 3D Terrestrial Laser Scanner come in a point cloud, and different types of surfaces such as spheres or planes can be modelled. Due to the occlusions and/or limited field of view, it is seldom possible to survey a complete feature from one location, and information has to be acquired from multiple points of view and later co-registered and geo-referenced to obtain a consistent coordinate system. The aim of this paper is not to match point clouds, but to show a methodology to adjust, following the traditional topo-geodetic methods, 3DTLS data by modelling references such as calibrated spheres and checker-boards to generate a 3D trilateration network from them to derive accuracy and reliability measurements and post-adjustment statistical analysis. The method tries to find the function that best fits the measured data, taking into account not only that the measurements made in the field are not perfect, but that each one of them has a different deviation depending on the adjustment of each reference, so they have to be weighted accordingly.

scholarly journals INFLUENCE OF RANGING UNCERTAINTY OF TERRESTRIAL LASER SCANNING ON CHANGE DETECTION IN TOPOGRAPHIC 3D POINT CLOUDS

2020 ◽  
Vol V-2-2020 ◽  
pp. 789-796 ◽  
Author(s):  
L. Winiwarter ◽  
K. Anders ◽  
D. Wujanz ◽  
B. Höfle
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Error Propagation ◽  
Laser Scanning ◽  
Incidence Angle ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Atomic Unit ◽  
3D Point Clouds

Abstract. Terrestrial laser scanners are commonly used for remotely sensing natural surfaces into 3D point clouds. Time series of such 3D point clouds can be analysed to gain information of surface changes that are induced by Earth surface shaping processes. The atomic unit in time series analysis is a bitemporal change detection and quantification. This should involve an estimation of the minimum quantifiable change, the Level of Detection, to separate signal from noise, e.g. stemming from the measurement. To enable such an estimation through error propagation, a model of the sensing instrument’s measurement uncertainty is required. In this work, we present an investigation on the ranging component of terrestrial laser scanning on this uncertainty and its influence on 3D distances between point clouds of two epochs. Specifically, we analyse the effects of incidence angle, intensity and range for different object materials, and make additional considerations with respect to waveform information returned by the sensor. We estimate a model for the rangefinder uncertainty of a terrestrial laser scanner and apply it on experimental data. The results show that using a sensor-specific model of ranging uncertainty allows an appropriate estimation of the Level of Detection. At a range of 60 m and a rotational displacement of 10°, this Level of Detection ranges between 0.1 mm to 1 mm for a white and a grey surface and up to 5 mm for a black surface. The completeness of the detection of significant change ranges from 60.2 % (black) to 89.8 % (grey) for the proposed method and from 65.5 % to 88.9 % for the baseline, when compared to tachymeter measurements. The similarity between the results is expected and suggests the validity of error propagation for the derivation of the Level of Detection.

scholarly journals THE EXAMPLE OF USING INTENSITY ORTHOIMAGES IN TLS DATA REGISTRATION – A CASE STUDY

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 467-474 ◽  
Author(s):  
J. S. Markiewicz
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Point Distribution ◽  
Data Registration ◽  
Depth Maps ◽  
Source Data ◽  
Panoramic Images

The paper presents the orientation analysis of terrestrial laser scanning (TLS) data. In the proposed data processing methodology, point clouds are considered as panoramic images and orthoimages enriched by depth maps. Computer vision (CV) algorithms were used for the orientation; they were applied to test the correctness of the detection of tie points, as well as the accuracy, number and point distribution. For the source data, point clouds acquired from the terrestrial laser scanner Z+F 50063h for the two chambers in the Museum of King John III’s Palace in Wilanów were utilized.

High-speed spatial change detection using point clouds and NDT

2017 ◽  
Vol 2017 (0) ◽  
pp. 2A2-O08
Author(s):  
Ukyou KATSURA ◽  
Ryo KURAZUME ◽  
Tomohide ISHIGAMI ◽  
Tsukasa OKADA
Keyword(s):  
Change Detection ◽  
High Speed ◽  
Point Clouds ◽  
Spatial Change

scholarly journals ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

2021 ◽  
Vol 6 (4) ◽  
pp. 8277-8284
Author(s):  
Balazs Nagy ◽  
Lorant Kovacs ◽  
Csaba Benedek
Keyword(s):  
Change Detection ◽  
Point Clouds ◽  
Deep Network
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