Probabilistic Approach to Mobile Robot Localization Based on Gaussian Models of Sensors

2014 ◽  
Vol 607 ◽  
pp. 803-810
Author(s):  
František Duchoň ◽  
Andrej Babinec ◽  
Jozef Rodina ◽  
Tomas Fico ◽  
Peter Hubinský

In this paper the probabilistic approach to mobile robot localization is discussed. Generally probabilistic localization uses some type of sensors model. In this paper Gaussian model, which is the most appropriate probabilistic model of the sensors, is used. The main body of the article deal with the proposal of own approach to probabilistic localization, which is inspired by Markov localization. That is why the Markov localization is described in the introduction of the article. At the end of the article several experiments with the real robot are described. Results of the experiments have proven that proposed localization is accurate, fast and reliable.

2019 ◽  
Vol 139 (9) ◽  
pp. 1041-1050
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Hidehiko Hosaka ◽  
Hironaga Miyamoto ◽  
Takashi Nakamura ◽  
...  

2021 ◽  
Author(s):  
Julio Fajardo ◽  
Victor Ferman ◽  
Jabes Guerra ◽  
Antonio Ribas Neto ◽  
Eric Rohmer

2009 ◽  
Vol 6 (3) ◽  
pp. 427-437 ◽  
Author(s):  
Ivan Paunovic ◽  
Darko Todorovic ◽  
Miroslav Bozic ◽  
Goran Djordjevic

The paper discusses a mobile robot localization. Due to cost and simplicity of signal processing, the ultrasonic sensors are very suitable for this application. However, their nonlinear characteristics requires thorough calibrating procedure in order to achieve reliable readings from the obstacles around the robot. Here we describe SMR400 ultrasonic sensor and its calibration procedure. The suggested calibration procedure was tested through a number of experiments, and the results are presented in this paper. .


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