Using Texture of Linear Objects for Build Enviroments Model and Navigations

2019 ◽  
Vol 20 (8) ◽  
pp. 490-497
Author(s):  
V. P. Noskov ◽  
I. O. Kiselev
Keyword(s):  
Vision System ◽  
Complete Solution ◽  
Control Object ◽  
Point Clouds ◽  
Video Camera ◽  
Urban Environments ◽  
Laser Sensor ◽  
Navigation Problem ◽  
Navigation Task ◽  
First Case

The actual tasks of 3D-reconstruction of the industrial-urban environment and navigation models are considered by solving the identification of textured linear objects in the process of movement according to the onboard complex and technical vision system consisting of a mutually adjusted 3D laser sensor and a video camera with a common viewing area. For a complete solution of the navigation task (determination of three linear and three angular coordinates of the control object), it is necessary to select and identify at least three mutually non-parallel flat objects in the process of moving in a sequence of point clouds formed by a 3D laser sensor. In the case of the allocation of less than three flat objects (for example, in environments subjected to destruction), the navigation problem is not fully solved (not all coordinates are determined unambiguously, and some coordinates are related by linear or non-linear dependencies). In these cases, it is proposed to additionally use the texture of the selected flat objects formed by the video camera. In the paper is given the analysis of the features of the solution of the navigation problem is carried out depending on the number of allocated and identifiable textured linear objects in the current integrated images and algorithms for solving the navigation problem are evaluated for selecting and identifying the process of movement of one textured linear object and of two textured non-parallel linear objects. It is shown that in the first case, the use of texture makes it possible to reduce the solution of the navigational problem to a three-dimensional one, and in the second case to a one-dimensional optimization problem (finding the global optimum of a functional three and one variable, respectively). The proposed algorithms for processing complexed images provide a complete solution to the navigation task even if less than three linear objects are selected, which significantly increases the reliability of solving the navigation task and building an environmental model even in industrial-urban environments that have been destroyed, and therefore, the reliability and survivability of the ground ones and airborne robotic tools in autonomous modes of movement. The results of the corresponding software and hardware solutions in real industrial-urban environments, confirmed the accuracy and effectiveness of the proposed algorithms.

scholarly journals Fixed Wing Aircraft Automatic Landing with the Use of a Dedicated Ground Sign System

Aerospace ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 167
Author(s):  
Bartłomiej Brukarczyk ◽  
Dariusz Nowak ◽  
Piotr Kot ◽  
Tomasz Rogalski ◽  
Paweł Rzucidło
Keyword(s):  
Embedded System ◽  
Final Section ◽  
Vision System ◽  
Video Camera ◽  
Hardware In The Loop ◽  
Glide Path ◽  
Automatic Landing ◽  
Input Signals ◽  
Fuzzy Logic Expert System ◽  
Longitudinal Channel

The paper presents automatic control of an aircraft in the longitudinal channel during automatic landing. There are two crucial components of the system presented in the paper: a vision system and an automatic landing system. The vision system processes pictures of dedicated on-ground signs which appear to an on-board video camera to determine a glide path. Image processing algorithms used by the system were implemented into an embedded system and tested under laboratory conditions according to the hardware-in-the-loop method. An output from the vision system was used as one of the input signals to an automatic landing system. The major components are control algorithms based on the fuzzy logic expert system. They were created to imitate pilot actions while landing the aircraft. Both systems were connected with one another for cooperation and to control an aircraft model in a simulation environment. Selected results of tests presenting control efficiency and precision are shown in the final section of the paper.

scholarly journals URBAN SCENE CLASSIFICATION USING FEATURES EXTRACTED FROM PHOTOGRAMMETRIC POINT CLOUDS ACQUIRED BY UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 511-518 ◽  
Author(s):  
G. G. Pessoa ◽  
R. C. Santos ◽  
A. C. Carrilho ◽  
M. Galo ◽  
A. Amorim
Keyword(s):  
Point Clouds ◽  
Bare Soil ◽  
Urban Environments ◽  
Data Sources ◽  
Classification Error ◽  
Error Matrix ◽  
Urban Scenes ◽  
Optical Images ◽  
Combining Data ◽  
Quantitative Analyses

<p><strong>Abstract.</strong> Images and LiDAR point clouds are the two major data sources used by the photogrammetry and remote sensing community. Although different, the synergy between these two data sources has motivated exploration of the potential for combining data in various applications, especially for classification and extraction of information in urban environments. Despite the efforts of the scientific community, integrating LiDAR data and images remains a challenging task. For this reason, the development of Unmanned Aerial Vehicles (UAVs) along with the integration and synchronization of positioning receivers, inertial systems and off-the-shelf imaging sensors has enabled the exploitation of the high-density photogrammetric point cloud (PPC) as an alternative, obviating the need to integrate LiDAR and optical images. This study therefore aims to compare the results of PPC classification in urban scenes considering radiometric-only, geometric-only and combined radiometric and geometric data applied to the Random Forest algorithm. For this study the following classes were considered: buildings, asphalt, trees, grass, bare soil, sidewalks and power lines, which encompass the most common objects in urban scenes. The classification procedure was performed considering radiometric features (Green band, Red band, NIR band, NDVI and Saturation) and geometric features (Height – nDSM, Linearity, Planarity, Scatter, Anisotropy, Omnivariance and Eigenentropy). The quantitative analyses were performed by means of the classification error matrix using the following metrics: overall accuracy, recall and precision. The quantitative analyses present overall accuracy of 0.80, 0.74 and 0.98 for classification considering radiometric, geometric and both data combined, respectively.</p>

scholarly journals CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 317-323
Author(s):  
J. Schachtschneider ◽  
C. Brenner
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Point Clouds ◽  
Urban Environments ◽  
Seasonal Effects ◽  
Data Set ◽  
Multi Temporal ◽  
One Year

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.

scholarly journals Technical vision system for apple defects recognition: justification, development, testing

2021 ◽  
Vol 59 (4) ◽  
pp. 488-500
Author(s):  
P. P. Kazakevich ◽  
A. N. Yurin ◽  
G. А. Prokopovich
Keyword(s):  
Vision System ◽  
Electronic Control Unit ◽  
Control Unit ◽  
Video Camera ◽  
Technological Scheme ◽  
High Productivity ◽  
Pests And Diseases ◽  
Technical Vision ◽  
Optical Module ◽  
Processing Line

The most rational method for identifying the quality of fruits is the optical method using PPE, which has the accuracy and stability of measurement, as well as distance and high productivity. The paper presents classification of fruit quality recognition systems and substantiates the design and technological scheme of the vision system for sorting them, consisting of an optical module with installed structural illumination and a video camera, an electronic control unit with an interface and actuators for the sorter and conveyor for fruits. In the course of the study, a single-stream type of fruit flow in PPE with forced rotation was substantiated, a structural and technological scheme of an STZ with a feeding conveyor, an optical module and a control unit, an algorithm for functioning of the STZ software was developed based on algorithm for segmentation of fruit colors, tracking algorithm, etc. deep learning ANN, which provide recognition of the size and color of fruits, as well as damage from mechanical stress, pests and diseases. The developed STZ has been introduced into the processing line for sorting and packing apples, LSP-4 has successfully passed preliminary tests and production tests at OJSC Ostromechevo. In the course of preliminary tests of the LSP-4 line, it was found that it provided fruit recognition with a probability of at least 95%, while the labor productivity made 2.5 t/h.

Welding Robot Welding Process Research and Development of Intelligent Technology

2013 ◽  
Vol 419 ◽  
pp. 774-777
Author(s):  
Ji Ming Yi ◽  
Min Han
Keyword(s):  
Vision System ◽  
Welding Process ◽  
Depth Information ◽  
Laser Sensor ◽  
Automatic Welding ◽  
Welding Robot ◽  
Existing Problems ◽  
Intelligent Technology ◽  
Welding Direction ◽  
Binocular Vision System

The welding direction of robot and existing problems, the groove plate is difficult to realize automatic welding robot problem, methods using laser sensor and a binocular vision system combines, image and depth information extraction plate groove groove, realize accurate 3D reconstruction.

scholarly journals A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 43 ◽  
Author(s):  
Rendong Wang ◽  
Youchun Xu ◽  
Miguel Angel Sotelo ◽  
Yulin Ma ◽  
Thompson Sarkodie-Gyan ◽  
...  
Keyword(s):  
Dynamic Environment ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Initial Position ◽  
Urban Environments ◽  
Data Set ◽  
Registration Method ◽  
Position Errors ◽  
Initial Errors ◽  
High Dynamic

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

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.

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