scholarly journals USE OF ASSISTED PHOTOGRAMMETRY FOR INDOOR AND OUTDOOR NAVIGATION PURPOSES

2015 ◽  
Vol XL-4/W5 ◽  
pp. 113-118 ◽  
Author(s):  
D. Pagliari ◽  
N. E. Cazzaniga ◽  
L. Pinto
Keyword(s):  
Urban Areas ◽  
Low Cost ◽  
Point Clouds ◽  
Satellite System ◽  
Indoor Navigation ◽  
Microsoft Kinect ◽  
Textured Surfaces ◽  
Navigation Problem ◽  
Additional Information ◽  
Outdoor Navigation

Nowadays, devices and applications that require navigation solutions are continuously growing. For instance, consider the increasing demand of mapping information or the development of applications based on users’ location. In some case it could be sufficient an approximate solution (e.g. at room level), but in the large amount of cases a better solution is required. <br><br> The navigation problem has been solved from a long time using Global Navigation Satellite System (GNSS). However, it can be unless in obstructed areas, such as in urban areas or inside buildings. An interesting low cost solution is photogrammetry, assisted using additional information to scale the photogrammetric problem and recovering a solution also in critical situation for image-based methods (e.g. poor textured surfaces). In this paper, the use of assisted photogrammetry has been tested for both outdoor and indoor scenarios. Outdoor navigation problem has been faced developing a positioning system with Ground Control Points extracted from urban maps as constrain and tie points automatically extracted from the images acquired during the survey. The proposed approach has been tested under different scenarios, recovering the followed trajectory with an accuracy of 0.20 m. <br><br> For indoor navigation a solution has been thought to integrate the data delivered by Microsoft Kinect, by identifying interesting features on the RGB images and re-projecting them on the point clouds generated from the delivered depth maps. Then, these points have been used to estimate the rotation matrix between subsequent point clouds and, consequently, to recover the trajectory with few centimeters of error.

scholarly journals GNSS SATELLITE VISIBILITY ANALYSIS BASED ON 3D SPATIAL INFORMATION IN URBAN AREAS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 123-128
Author(s):  
Y.-H. Lu ◽  
J.-Y. Han
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
Spatial Information ◽  
Signal Transmission ◽  
Point Clouds ◽  
Satellite System ◽  
Topographic Effects ◽  
Analysis Technique ◽  
Satellite Visibility ◽  
Global Navigation Satellite

Abstract. Global Navigation Satellite System (GNSS) is a matured modern technique for spatial data acquisition. Its performance has a great correlation with GNSS receiver position. However, high-density building in urban areas causes signal obstructions and thus hinders GNSS’s serviceability. Consequently, GNSS positioning is weakened in urban areas, so deriving proper improvement resolutions is a necessity. Because topographic effects are considered the main factor that directly block signal transmission between satellites and receivers, this study integrated aerial borne LiDAR point clouds and a 2D building boundary map to provide reliable 3D spatial information to analyze topographic effects. Using such vector data not only reflected high-quality GNSS satellite visibility calculations, but also significantly reduced data amount and processing time. A signal obstruction analysis technique and optimized computational algorithm were also introduced. In conclusion, this paper proposes using superimposed column method to analyze GNSS receivers’ surrounding environments and thus improve GNSS satellite visibility predictions in an efficient and reliable manner.

scholarly journals MOVING OBJECT CLASSIFICATION USING MULTILAYER LASER SCANNING WITH SPACE SUBDIVISION FRAMEWORK

2020 ◽  
Vol V-4-2020 ◽  
pp. 103-108
Author(s):  
M. Nakagawa ◽  
M. Taguchi
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Object Classification ◽  
Point Clouds ◽  
Satellite System ◽  
Space Subdivision ◽  
Position Data ◽  
Global Navigation Satellite

Abstract. In this paper, we focus on the development of intelligent construction vehicles to improve the safety of workers in construction sites. Generally, global navigation satellite system positioning is utilized to obtain the position data of workers and construction vehicles. However, construction fields in urban areas have poor satellite positioning environments. Therefore, we have developed a 3D sensing unit mounted on a construction vehicle for worker position data acquisition. The unit mainly consists of a multilayer laser scanner. We propose a real-time object measurement, classification and tracking methodology with the multilayer laser scanner. We also propose a methodology to estimate and visualize object behaviors with a spatial model based on a space subdivision framework consisting of agents, activities, resources, and modifiers. We applied the space subdivision framework with a geofencing approach using real-time object classification and tracking results estimated from temporal point clouds. Our methodology was evaluated using temporal point clouds acquired from a construction vehicle in drilling works.

scholarly journals Performance evaluation of GNSS receiver clock modelling in urban navigation using geodetic and high-sensitivity receivers

2021 ◽  
pp. 1-19
Author(s):  
Ankit Jain ◽  
Steffen Schön
Keyword(s):  
Urban Areas ◽  
Low Cost ◽  
High Sensitivity ◽  
Satellite System ◽  
Position Estimation ◽  
Vertical Coordinate ◽  
Gnss Receiver ◽  
Position Errors ◽  
Receiver Clock ◽  
External Clock

Abstract In urban areas, the Global Navigation Satellite System (GNSS) can lead to position errors of tens of meters due to signal obstruction and severe multipath effects. In cases of 3D-positioning, the vertical coordinate is estimated less accurately than are the horizontal coordinates. Multisensor systems can enhance navigation performance in terms of accuracy, availability, continuity and integrity. However, the addition of multiple sensors increases the system cost, and thereby the applicability to low-cost applications is limited. By using the concept of receiver clock modelling (RCM), the position estimation can be made more robust; the use of high-sensitivity (HS) GNSS receivers can improve the system availability and continuity. This paper investigates the integration of a low-cost HS GNSS receiver with an external clock in urban conditions; subsequently, the gain in the navigation performance is evaluated. GNSS kinematic data is recorded in an urban environment with multiple geodetic-grade and HS receivers. The external clock stability information is incorporated through the process noise matrix in a Kalman filter when estimating the position, velocity and time states. Results shows that the improvement in the precision of the height component and vertical velocity with both receivers is about 70% with RCM compared with the estimates obtained without applying RCM. Pertaining accuracy, the improvement in height with RCM is found to be about 70% and 50% with geodetic and HS receivers, respectively. In terms of availability, the HS receiver delivers an 100% output compared with a geodetic receiver, which provides an output 99⋅4% of the total experiment duration.

scholarly journals PROJECTOR-BASED AUGMENTED REALITY FOR QUALITY INSPECTION OF SCANNED OBJECTS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 83-90 ◽  
Author(s):  
J. Kern ◽  
M. Weinmann ◽  
S. Wursthorn
Keyword(s):  
Augmented Reality ◽  
Low Cost ◽  
Point Clouds ◽  
Camera System ◽  
Additional Information ◽  
Generic Term ◽  
3D Information ◽  
Monitoring Information ◽  
Surface Changes

After scanning or reconstructing the geometry of objects, we need to inspect the result of our work. Are there any parts missing? Is every detail covered in the desired quality? We typically do this by looking at the resulting point clouds or meshes of our objects on-screen. What, if we could see the information directly visualized on the object itself? Augmented reality is the generic term for bringing virtual information into our real environment. In our paper, we show how we can project any 3D information like thematic visualizations or specific monitoring information with reference to our object onto the object’s surface itself, thus augmenting it with additional information. For small objects that could for instance be scanned in a laboratory, we propose a low-cost method involving a projector-camera system to solve this task. The user only needs a calibration board with coded fiducial markers to calibrate the system and to estimate the projector’s pose later on for projecting textures with information onto the object’s surface. Changes within the projected 3D information or of the projector’s pose will be applied in real-time. Our results clearly reveal that such a simple setup will deliver a good quality of the augmented information.

3D reconstruction of human head based on consumer RGB-D sensors

2020 ◽  
pp. 2050061
Author(s):  
Zihan Liu ◽  
Guanghong Gong ◽  
Ni Li ◽  
Zihao Yu
Keyword(s):  
3D Reconstruction ◽  
Low Cost ◽  
Three Dimensional ◽  
Human Head ◽  
Point Clouds ◽  
Microsoft Kinect ◽  
Head Model ◽  
Registration Method ◽  
Three Dimensional Modeling ◽  
Texture Generation

Three-dimensional (3D) reconstruction of a human head with high precision has promising applications in scientific research, product design and other fields. However, it still faces resistance from two factors. One is inaccurate registration caused by symmetrical distribution of head feature points, and the other is economic burden due to high-accuracy sensors. Research on 3D reconstruction with portable consumer RGB-D sensors such as the Microsoft Kinect has been highlighted in recent years. Based on our multi-Kinect system, a precise and low-cost three-dimensional modeling method and its system implementation are introduced in this paper. A registration method for multi-source point clouds is provided, which can reduce the fusion differences and reconstruct the head model accurately. In addition, a template-based texture generation algorithm is presented to generate a fine texture. The comparison and analysis of our experiments show that our method can reconstruct a head model in an acceptable time with less memory and better effect.

scholarly journals PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS

2016 ◽  
Vol XLI-B7 ◽  
pp. 945-952 ◽  
Author(s):  
V. Petras ◽  
A. Petrasova ◽  
J. Jeziorska ◽  
H. Mitasova
Keyword(s):  
Open Source ◽  
Point Cloud ◽  
Low Cost ◽  
Point Clouds ◽  
Physical Models ◽  
Microsoft Kinect ◽  
Surface Model ◽  
3D Scanner ◽  
Grass Gis ◽  
Point Data

Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.

scholarly journals COMPARISON BETWEEN RGB AND RGB-D CAMERAS FOR SUPPORTING LOW-COST GNSS URBAN NAVIGATION

2018 ◽  
Vol XLII-2 ◽  
pp. 991-998 ◽  
Author(s):  
L. Rossi ◽  
C. I. De Gaetani ◽  
D. Pagliari ◽  
E. Realini ◽  
M. Reguzzoni ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Urban Areas ◽  
Ad Hoc ◽  
Focal Length ◽  
Low Cost ◽  
Experimental Tests ◽  
Microsoft Kinect ◽  
Visual Data ◽  
Data Accessibility

A pure GNSS navigation is often unreliable in urban areas because of the presence of obstructions, thus preventing a correct reception of the satellite signal. The bridging between GNSS outages, as well as the vehicle attitude reconstruction, can be recovered by using complementary information, such as visual data acquired by RGB-D or RGB cameras. In this work, the possibility of integrating low-cost GNSS and visual data by means of an extended Kalman filter has been investigated. The focus is on the comparison between the use of RGB-D or RGB cameras. In particular, a Microsoft Kinect device (second generation) and a mirrorless Canon EOS M RGB camera have been compared. The former is an interesting RGB-D camera because of its low-cost, easiness of use and raw data accessibility. The latter has been selected for the high-quality of the acquired images and for the possibility of mounting fixed focal length lenses with a lower weight and cost with respect to a reflex camera. The designed extended Kalman filter takes as input the GNSS-only trajectory and the relative orientation between subsequent pairs of images. Depending on the visual data acquisition system, the filter is different because RGB-D cameras acquire both RGB and depth data, allowing to solve the scale problem, which is instead typical of image-only solutions. The two systems and filtering approaches were assessed by ad-hoc experimental tests, showing that the use of a Kinect device for supporting a u-blox low-cost receiver led to a trajectory with a decimeter accuracy, that is 15&amp;thinsp;% better than the one obtained when using the Canon EOS M camera.

scholarly journals GENERATION OF INDOOR POINT CLOUDS EXPLOITING GEOMETRIC SYMMETRIES AND REGULARITIES

2021 ◽  
Vol XLVI-M-1-2021 ◽  
pp. 435-440
Author(s):  
A. Masiero ◽  
A. Guarnieri ◽  
G. Tucci ◽  
A. Vettore
Keyword(s):  
Laser Scanning ◽  
Ad Hoc ◽  
Inertial Sensors ◽  
Low Cost ◽  
Point Clouds ◽  
Satellite System ◽  
Mobile Mapping ◽  
Suitable Alternative ◽  
Planar Surfaces ◽  
Mapping System

Abstract. 3D building modeling is becoming an important support in civil engineering, architecture and cultural heritage applications. Despite static laser scanning can be considered as the state-of-the-art in such kind of applications, mobile mapping techniques can be considered as a suitable alternative to quickly gather geospatial information. Outdoor mobile mapping can be considered as a mature technique, which takes into advantage of the Global Navigation Satellite System (GNSS)-laser scanning information fusion. Instead, indoor mobile mapping is typically more challenging: the unavailability of GNSS makes the mapping system rely either just on the inertial navigation system, or on some control points. A drift in the navigation solution, and consequently in the 3D reconstruction, is typically visible after a while in the former case, whereas the use of other surveying instruments is required in the latter.This work aims at exploiting geometric characteristics of the buildings, such as symmetries and regularities, to reduce the drift effect in indoor mobile mapping, in particular when dealing with affordable systems. The proposed approach is based on the segmentation of the point clouds acquired with a time of flight camera (ToF), detecting in particular vertical planar surfaces. It is well known that aligning planar surfaces can be a viable way for reducing the drift in this kind of applications. Nevertheless, this paper aims also at investigating the use of geometric symmetries to such aim.The proposed approach is tested on a case study, a building of the University of Padova, whose reconstruction was produced by an ad hoc affordable mobile mapping system, integrating low cost inertial sensors, RGB and ToF camera.

A Computation Effective Range-Based 3D Mapping Aided GNSS with NLOS Correction Method

2020 ◽  
Vol 73 (6) ◽  
pp. 1202-1222 ◽  
Author(s):  
Hoi-Fung Ng ◽  
Guohao Zhang ◽  
Li-Ta Hsu
Keyword(s):  
Ray Tracing ◽  
Urban Areas ◽  
Low Cost ◽  
Three Dimensional ◽  
Correction Method ◽  
Satellite System ◽  
Urban Environments ◽  
3D Mapping ◽  
Gnss Positioning ◽  
Positioning Method

Global navigation satellite system (GNSS) positioning in dense urban areas remains a challenge due to the signal reflection by buildings, namely multipath and non-line-of-sight (NLOS) reception. These effects degrade the performance of low-cost GNSS receivers such as in those smartphones. An effective three-dimensional (3D) mapping aided GNSS positioning method is proposed to correct the NLOS error. Instead of applying ray-tracing simulation, the signal reflection points are detected based on a skyplot with the surrounding building boundaries. The measurements of the direct and reflected signals can thus be simulated and further used to determine the user's position based on the measurement likelihood between real measurements. Verified with real experiments, the proposed algorithm is able to reduce the computational load greatly while maintaining a positioning accuracy within 10 metres of error in dense urban environments, compared with the conventional method of ray-tracing based NLOS corrected positioning.

scholarly journals PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS

2016 ◽  
Vol XLI-B7 ◽  
pp. 945-952 ◽  
Author(s):  
V. Petras ◽  
A. Petrasova ◽  
J. Jeziorska ◽  
H. Mitasova
Keyword(s):  
Open Source ◽  
Point Cloud ◽  
Low Cost ◽  
Point Clouds ◽  
Physical Models ◽  
Microsoft Kinect ◽  
Surface Model ◽  
3D Scanner ◽  
Grass Gis ◽  
Point Data

Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.

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