scholarly journals ZIGBEE WIRELESS NETWORK-BASED INDOOR MOBILE ROBOT MONITORING AND POSITIONING METHOD UNDER BINOCULAR VISION

2020 ◽  
Vol i (8) ◽  
Keyword(s):  
Mobile Robot ◽  
Wireless Network ◽  
Binocular Vision ◽  
Indoor Mobile Robot ◽  
Positioning Method

BUILDING MAP AND POSITIONING SYSTEM FOR INDOOR ROBOT BASED ON PLAYER

2011 ◽  
Vol 08 (04) ◽  
pp. 281-290
Author(s):  
BIN WANG ◽  
WEI LU ◽  
BIN KONG
Keyword(s):  
Mobile Robot ◽  
Positioning System ◽  
Map Building ◽  
Adaptive Monte Carlo ◽  
Indoor Mobile Robot ◽  
Robot Positioning ◽  
Positioning Method ◽  
Dynamic Map ◽  
Slam Algorithm ◽  
Number Of Particles

In this paper, we have proposed a map-building and positioning method for an indoor mobile robot based on the open source platform Player. First, the DP-SLAM algorithm is transplanted to the Player and used to build the dynamic offline map. This would reduce the errors and constraints caused by manual map building. Second, the KLD-Sampling Adaptive Monte Carlo Locating (KLD-AMCL) algorithm is introduced to reduce the number of particles required in locating. Meanwhile, higher accuracy of localization is achieved through calculating the MLE and the real posterior KL distance. Finally, an indoor mobile robot positioning system is built by combining the Player platform, dynamic map building and KLD-AMCL algorithm. Experimental results show that the proposed system has better environmental adaptability and higher positioning accuracy.

Global Localization for Indoor Mobile Robot Based on Binocular Vision

2020 ◽  
Vol 57 (4) ◽  
pp. 041503
Author(s):  
李鹏 Li Peng ◽  
张洋洋 Zhang Yangyang
Keyword(s):  
Mobile Robot ◽  
Binocular Vision ◽  
Global Localization ◽  
Indoor Mobile Robot

Statistical Classification Based Fast Drivable Region Detection for Indoor Mobile Robot

2014 ◽  
Vol 11 (01) ◽  
pp. 1450010 ◽  
Author(s):  
Shengyue Qu ◽  
Cai Meng
Keyword(s):  
Mobile Robot ◽  
Binocular Vision ◽  
Fast Method ◽  
Feature Points ◽  
Statistical Classification ◽  
Region Detection ◽  
Indoor Mobile Robot ◽  
Vanishing Line

A fast method based on binocular vision is proposed for mobile robot to detect drivable regions. At first, the image is segmented into regions, and some obstacles are determined by the ground vanishing line. Then, according to the different distribution of feature points extracted from the left regions, we propose two approaches to classify regions: region determination based on feature statistical classification for regions with rich feature points and region determination based on area statistical classification for regions with sparse feature points. Finally, we get the drivable regions by combination of the two approaches. The results of indoor experiments show that the method can perform quickly and robustly.

Precise positioning method for multi-process connecting based on binocular vision

2016 ◽  
Author(s):  
Wei Liu ◽  
Lichao Ding ◽  
Kai Zhao ◽  
Xiao Li ◽  
Ling Wang ◽  
...  
Keyword(s):  
Binocular Vision ◽  
Precise Positioning ◽  
Positioning Method
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