Detection of Landmarks by Autonomous Mobile Robots Using Camera-Based Sensors in Outdoor Environments

Poliarus, Oleksandr; Poliakov, Yevhen; Lebedynskyi, Andrii

doi:10.1109/jsen.2020.3010883

Cited by 8 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such a function can be a test function, that is, defined at the initial time with sufficient accuracy. Thus, each point in multidimen-rithm similar to that developed in [24]. One pattern, for in the size of the pattern means a different probability of example, a rectangle, an ellipse, etc., is superimposed on CHG jumps detection for a given value of the probability the obtained characteristic images (Fig.…”

Section: Description Of the Experimental Research Resultsmentioning

confidence: 99%

Visualization method for multidimentional random processes

Poliarus¹,

Lebedynskyi²,

Chepusenko³

et al. 2023

MEM

Self Cite

View full text Add to dashboard Cite

The article proposes a method for visualizing multidimensional random process realizations using the example of the concentrations of harmful gases emitted into the atmosphere from a thermal power plant. The method is based on the transformation of gas concentration values in one point of multidimensional space at the same time into a two-dimensional curve, which is described by the sum of products of normalized concentrations by orthogonal Legendre functions of the corresponding order. The combination of such curves on a two-dimensional plane at discrete times creates a characteristic image that can be used to visually detect features of gas concentrations over time by a human operator.

show abstract

Section: Description Of the Experimental Research Resultsmentioning

confidence: 99%

Visualization method for multidimentional random processes

Poliarus¹,

Lebedynskyi²,

Chepusenko³

et al. 2023

MEM

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [11], a method is proposed for expanding the conditions for the use of radars based on the developed system for detecting jumps in the amplitude of signals reflected from a landmark. In passive location with the use of video cameras or night vision devices that receive waves of different ranges (optical, visible and infrared), it becomes possible to identify landmarks with a high probability using the methods described in [12]. Taking into account the fact that the possibility of identifying landmarks significantly depends on the surrounding background and the illumination of the area for individual measuring instruments, let's give an example of creating an information cadastre of a land landmark.…”

Section: Research Results and Discussionmentioning

confidence: 99%

Model and method development for determining the completeness of information for remote detection of landmarks for autonomous mobile robots

Poliarus

Lebedynskyi

Chepusenko

et al. 2021

TAPR

Self Cite

View full text Add to dashboard Cite

The object of research is the completeness of information for making a navigation decision by an autonomous mobile robot when it performs a task in an unfamiliar area without GPS. It is difficult to identify a landmark in the absence and abundance of information. One of the most problematic places is the mathematical description of the criterion according to which an autonomous robot makes a decision about the completeness of information. The paper substantiates a model and method for determining the completeness of information by a robot equipped with several landmarks detection tools operating on different physical principles. It is shown that the implementation of the method requires a priori information on the probability of detecting various landmarks by passive and active means against a continuous and discontinuous background at different illumination of objects, in day and night conditions under different weather conditions. The values of the probability of detecting a specific type of landmark obtained in such studies serve as the basis for constructing an information cadastre for a job performing tasks on the ground. Three formulas are proposed for determining the coefficient of completeness of information, taking into account a priori and a posteriori inventories, and recommended areas of application. The value of this coefficient depends on the threshold level of the probability of detecting a landmark. The reliability of a decision made by a robot is greatest when it is made under conditions of a certain level of completeness of information. The proposed method can be used for other technical objects from which the measurement information is received. Compared with the known methods, it expands the boundaries of application and reveals the possibility of assessing the completeness of information in constantly changing conditions. Along with a change in these conditions, the characteristics of the completeness of information also change. The coefficient of completeness of information can approach unity even in the absence of separate means of detecting landmarks, and then the method makes it possible to assess the need for their use in the given conditions.

show abstract

“…Таким чином, у більшості випадків досягається необхідна ефективність запропонованого способу, тобто задано ймовірність виявлення дефекту МС за фіксованої ймовірності хибної тривоги. Аналогічний підхід був використаний нами при виявленні орієнтирів для автономних мобільних роботів [19].…”

Section: сутність методуunclassified

Method, models and information technology of decision making about the state of technical objects under non-stationary input influences

Poliarus¹,

Lebedynskyi²,

Сhepusenko³

2021

Bulletin of KhNAHU

View full text Add to dashboard Cite

There is a significant increase in the amount of measuring information at complex and large technical objects, such as bridges. Decision making about these objects states under non-stationary input influences is a difficult task. The article proposes to make the transition from single-channel information processing to multi-channel one. Each channel processes one of the Hilbert-Huang modes, into which each realization of the nonstationary signal decomposes. It is shown that the first three modes of decomposition are often enough, which in most cases create a stationary process. If a mode is non-stationary, it is possible to decompose it into these modes. The final decision according to statistical criteria is made not on realizations as it is traditionally carried out, but on Hilbert-Huang modes.

show abstract

Detection of Landmarks by Autonomous Mobile Robots Using Camera-Based Sensors in Outdoor Environments

Cited by 8 publications

References 16 publications

Visualization method for multidimentional random processes

Visualization method for multidimentional random processes

Model and method development for determining the completeness of information for remote detection of landmarks for autonomous mobile robots

Method, models and information technology of decision making about the state of technical objects under non-stationary input influences

Contact Info

Product

Resources

About