scholarly journals Drift-Aware Monocular Localization Based on a Pre-Constructed Dense 3D Map in Indoor Environments

2018 ◽  
Vol 7 (8) ◽  
pp. 299 ◽  
Author(s):  
Guanyuan Feng ◽  
Lin Ma ◽  
Xuezhi Tan ◽  
Danyang Qin
Keyword(s):  
Relative Position ◽  
Detection Method ◽  
Weighted Least Squares ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Feature Points ◽  
Line Model ◽  
Localization System ◽  
The Absolute ◽  
Global And Local

Recently, monocular localization has attracted increased attention due to its application to indoor navigation and augmented reality. In this paper, a drift-aware monocular localization system that performs global and local localization is presented based on a pre-constructed dense three-dimensional (3D) map. In global localization, a pixel-distance weighted least squares algorithm is investigated for calculating the absolute scale for the epipolar constraint. To reduce the accumulative errors that are caused by the relative position estimation, a map interaction-based drift detection method is introduced in local localization, and the drift distance is computed by the proposed line model-based maximum likelihood estimation sample consensus (MLESAC) algorithm. The line model contains a fitted line segment and some visual feature points, which are used to seek inliers of the estimated feature points for drift detection. Taking advantage of the drift detection method, the monocular localization system switches between the global and local localization modes, which effectively keeps the position errors within an expected range. The performance of the proposed monocular localization system is evaluated on typical indoor scenes, and experimental results show that compared with the existing localization methods, the accuracy improvement rates of the absolute position estimation and the relative position estimation are at least 30.09% and 65.59%, respectively.

scholarly journals Indoor Navigation Using Particle Filter and Sensor Fusion

2012 ◽  
Vol 19 (2) ◽  
pp. 31-40
Author(s):  
Lukas Köping ◽  
Thomas Mühsam ◽  
Christian Ofenberg ◽  
Bernhard Czech ◽  
Michael Bernard ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Particle Filter ◽  
Indoor Localization ◽  
High Accuracy ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Sensor Data ◽  
Localization System ◽  
Multiple Sensor ◽  
Large Building

Abstract In this paper we present an indoor localization system based on particle filter and multiple sensor data like acceleration, angular velocity and compass data. With this approach we tackle the problem of documentation on large building yards during the construction phase. Due to the circumstances of such an environment we cannot rely on any data from GPS, Wi-Fi or RFID. Moreover this work should serve us as a first step towards an all-in-one navigation system for mobile devices. Our experimental results show that we can achieve high accuracy in position estimation.

Indoor localization system in a multi-block workspace

Robotica ◽  
2009 ◽  
Vol 28 (3) ◽  
pp. 397-403 ◽  
Author(s):  
JaeHyun Park ◽  
MunGyu Choi ◽  
YunFei Zu ◽  
JangMyung Lee
Keyword(s):  
Mobile Robots ◽  
Indoor Localization ◽  
Ultrasonic Attenuation ◽  
Low Cost ◽  
Estimation Method ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Service Robots ◽  
Localization System ◽  
Ubiquitous Technology

SUMMARYThis paper proposes methodologies and techniques for multi-block navigation of an indoor localization system with active beacon sensors. As service robots and ubiquitous technology have evolved, there is an increasing need for autonomous indoor navigation of mobile robots. In a large number of indoor localization schemes, the absolute position estimation method, relying on navigation beacons or landmarks, has been widely used due to its low cost and high accuracy. However, few of these schemes have managed to expand the applications for use in complicated workspaces involving many rooms or blocks that cover a wide region, such as airports and stations. Since the precise and safe navigation of mobile robots in complicated workspaces is vital for the ubiquitous technology, it is necessary to develop a multi-block navigation scheme. This new design of an indoor localization system includes ultrasonic attenuation compensation, dilution of both the precision analysis and fault detection, and an isolation algorithm using redundant measurements. These ideas are implemented on actual mobile robot platforms and beacon sensors, and experimental results are presented to test and demonstrate the new methods.

Experimental research of indoor navigation system based on broadcast signals

2020 ◽  
Vol 49 (5) ◽  
pp. 49-57
Author(s):  
A. V. Ksendzuk ◽  
E. A. Surmin ◽  
V. V. Kachesov ◽  
S. O. Zhdanov ◽  
K. S. Shakhalov
Keyword(s):  
Navigation System ◽  
Open Space ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Estimation Accuracy ◽  
Satellite Systems ◽  
Indoor Navigation System ◽  
Point To Point ◽  
Navigation Signals ◽  
Navigation Equipment

Results of an experimental study of a local navigation system based on the processing signals from broadcast sources presented. The results of the development of processing algorithms for point-to-point coordinates estimation of the object are presented. The results of the development of algorithms for trajectories estimation are presented. In performed simulation the possibility of obtaining submeter position estimation accuracy in the proposed system is shown. Development results of the navigation module demonstrator are presented. The results of experimental work in difficult navigation conditions, in the presence of shading, reflections and other factors, are presented. It is shown that the developed navigation module allows in the open space near buildings which partially obscuring the satellite systems signals to obtain accuracy higher than the GNSS navigation equipment. In indoor environment in the absence of satellite navigation signals, the developed module shows positioning accuracy not worse than 1.5 meters and provides a measurement rate 1 Hz and better.

scholarly journals Passive Measurement of Three Optical Beacon Coordinates Using a Simultaneous Method

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5235
Author(s):  
Jiri Nemecek ◽  
Martin Polasek
Keyword(s):  
Mathematical Model ◽  
Relative Position ◽  
Experimental Tests ◽  
Image Sensor ◽  
Geometric Parameters ◽  
Light Sources ◽  
Feature Points ◽  
Passive Measurement ◽  
The Mathematical Model ◽  
Physical Principles

Among other things, passive methods based on the processing of images of feature points or beacons captured by an image sensor are used to measure the relative position of objects. At least two cameras usually have to be used to obtain the required information, or the cameras are combined with other sensors working on different physical principles. This paper describes the principle of passively measuring three position coordinates of an optical beacon using a simultaneous method and presents the results of corresponding experimental tests. The beacon is represented by an artificial geometric structure, consisting of several semiconductor light sources. The sources are suitably arranged to allow, all from one camera, passive measurement of the distance, two position angles, the azimuth, and the beacon elevation. The mathematical model of this method consists of working equations containing measured coordinates, geometric parameters of the beacon, and geometric parameters of the beacon image captured by the camera. All the results of these experimental tests are presented.

An adaptive localization system for outdoor/indoor navigation for autonomous robots

2006 ◽  
Author(s):  
E. B. Pacis ◽  
B. Sights ◽  
G. Ahuja ◽  
G. Kogut ◽  
H. R. Everett
Keyword(s):  
Autonomous Robots ◽  
Indoor Navigation ◽  
Localization System

scholarly journals Relative Position Estimation Between Two UWB Devices With IMUs

2021 ◽  
Vol 6 (3) ◽  
pp. 4313-4320
Author(s):  
Charles Champagne Cossette ◽  
Mohammed Shalaby ◽  
David Saussie ◽  
James Richard Forbes ◽  
Jerome Le Ny
Keyword(s):  
Relative Position ◽  
Position Estimation

Position Estimation of Single Camera Visual System Based on Total Least Squares Algorithm

2012 ◽  
Vol 239-240 ◽  
pp. 1352-1355
Author(s):  
Jing Zhou ◽  
Yin Han Gao ◽  
Chang Yin Liu ◽  
Ji Zhi Li
Keyword(s):  
Visual System ◽  
Least Squares ◽  
Total Least Squares ◽  
Position Estimation ◽  
Feature Points ◽  
Perspective Theory ◽  
Optical Feature ◽  
The Matrix ◽  
Least Squares Algorithm ◽  
Set Up

The position estimation of optical feature points of visual system is the focus factor of the precision of system. For this problem , to present the Total Least Squares Algorithm . Firstly , set up the measurement coordinate system and 3D model between optical feature points, image points and the position of camera according to the position relation ; Second , build the matrix equations between optical feature points and image points ; Then apply in the total least squares to have an optimization calculation ; Finally apply in the coordinate measuring machining to have a simulation comparison experiment , the results indicate that the standard tolerance of attitude coordinate calculated by total least squares is 0.043mm, it validates the effectiveness; Compare with the traditional method based on three points perspective theory, measure the standard gauge of 500mm; the standard tolerance of traditional measurement system is 0.0641mm, the standard tolerance of Total Least Squares Algorithm is 0.0593mm; The experiment proves the Total Least Squares Algorithm is effective and has high precision.

scholarly journals Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 122
Author(s):  
Yang Li ◽  
Fangyuan Ma ◽  
Cheng Ji ◽  
Jingde Wang ◽  
Wei Sun
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Chemical Process ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Normal Operation ◽  
Discriminative Feature ◽  
Inherent Feature ◽  
Global And Local

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

Sign in / Sign up

Export Citation Format

Share Document