1A2-I02 Development of navigation system for mobile robot using Graph SLAM and local occupancy grid maps(Localization and Mapping (1))

2013 ◽  
Vol 2013 (0) ◽  
pp. _1A2-I02_1-_1A2-I02_3
Author(s):  
Taichi Itoh ◽  
Kazuyuki Morioka
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Occupancy Grid ◽  
Localization And Mapping ◽  
Occupancy Grid Maps ◽  
Graph Slam

scholarly journals A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6988
Author(s):  
Shuien Yu ◽  
Chunyun Fu ◽  
Amirali K. Gostar ◽  
Minghui Hu
Keyword(s):  
Multiple Robots ◽  
Working Mechanism ◽  
Topological Maps ◽  
Occupancy Grid ◽  
Localization And Mapping ◽  
Robot Systems ◽  
Feature Based ◽  
Occupancy Grid Maps ◽  
Multi Robot ◽  
Map Merging

When multiple robots are involved in the process of simultaneous localization and mapping (SLAM), a global map should be constructed by merging the local maps built by individual robots, so as to provide a better representation of the environment. Hence, the map-merging methods play a crucial rule in multi-robot systems and determine the performance of multi-robot SLAM. This paper looks into the key problem of map merging for multiple-ground-robot SLAM and reviews the typical map-merging methods for several important types of maps in SLAM applications: occupancy grid maps, feature-based maps, and topological maps. These map-merging approaches are classified based on their working mechanism or the type of features they deal with. The concepts and characteristics of these map-merging methods are elaborated in this review. The contents summarized in this paper provide insights and guidance for future multiple-ground-robot SLAM solutions.

Navigation Based on Metric Route Information in Places Where the Mobile Robot Visits for the First Time

2019 ◽  
Vol 31 (2) ◽  
pp. 180-193
Author(s):  
Asahi Handa ◽  
◽  
Azumi Suzuki ◽  
Hisashi Date ◽  
Ryohsuke Mitsudome ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Navigation System ◽  
Geometric Accuracy ◽  
Localization And Mapping ◽  
Route Information ◽  
Set Up ◽  
The University ◽  
Local Map ◽  
First Time

In this study, we propose a navigation system that guides a robot at a location visited for the first time, without developing a map in advance. First, it estimates the position of a path that exists on the local map by matching the metric route information and the local map generated by simultaneous localization and mapping (SLAM); this is achieved by using a particle filter. Then, the robot travels to the destination along the estimated route. In this system, the geometric accuracy of the route information specified in advance and the accuracy of the map generated by SLAM are essential. Furthermore, it is necessary to recognize the traversable area. The experiment performed verifies the matching of the route information and local map. In the autonomous running experiment, we conduct a trial run on a course set up at the University of Tsukuba.

scholarly journals Implementation and Reconfiguration of Robot Operating System on Human Follower Transporter Robot

2015 ◽  
Vol 9 (2) ◽  
pp. 59
Author(s):  
Addythia Saphala ◽  
Prianggada Indra Tanaya
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
Navigation System ◽  
Robot Navigation ◽  
Simultaneous Localization And Mapping ◽  
Kinect Sensor ◽  
Operation System ◽  
Robot Operating System ◽  
Localization And Mapping

Robotic Operation System (ROS) is an im- portant platform to develop robot applications. One area of applications is for development of a Human Follower Transporter Robot (HFTR), which  can  be  considered  as a custom mobile robot utilizing differential driver steering method and equipped with Kinect sensor. This study discusses the development of the robot navigation system by implementing Simultaneous Localization and Mapping (SLAM).

Mobile Robots Navigation, Mapping, and Localization Part II

2011 ◽  
pp. 1080-1088
Author(s):  
Lee Gim Hee ◽  
Marcelo H. Ang Jr.
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Map Building ◽  
Geometric Properties ◽  
Topological Maps ◽  
Occupancy Grid ◽  
Topological Mapping ◽  
Grid Mapping ◽  
Occupancy Grid Mapping ◽  
Occupancy Grid Maps

In addition to the capability to navigate from a point of origin to a given goal and avoiding all static and dynamic obstacles, a mobile robot must posses another two competencies: map building and localization in order to be useful. A mobile robot acquires information of its environment via the process of map building. Map building for mobile robots are commonly divided into occupancy grid and topological maps. Occupancy-grid maps seek to represent the geometric properties of the environment. Occupancy-grid mapping was first suggested by Elfes in 1987 and the idea was published in his Ph.D. thesis (A. Elfes, 1989) in 1989. Topological mapping was first introduced in 1985 as an alternative to the occupancy- grid mapping by R. Chatila and J.-P. Laumond (R. Chatila, & J.-P. Laumond, 1985). Topological maps describe the connectivity of different locations in the environment. The pose of a mobile robot must be known at all times for it to navigation and build a map accurately. This is the problem of localization and it was first described in the late 1980’s by R. Smith et al (R. Smith et al, 1980). Some key algorithms for map building and localization will be discussed in this article.

Sign in / Sign up

Export Citation Format

Share Document