scholarly journals Map-Merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation

2020 ◽  
pp. 46-60
Author(s):  
Andrey Bokovoy ◽  
Kirill Muraviev ◽  
Konstantin Yakovlev
Keyword(s):  
Feasibility Study ◽  
Empirical Evaluation ◽  
Visual Slam ◽  
Merging Algorithms ◽  
Map Merging

Interactive Media for Communication / Advertising - a Feasibility Study and First Trial

1993 ◽  
Vol 37 (9) ◽  
pp. 635-639
Author(s):  
Rae Burns ◽  
Gerhard Deffner
Keyword(s):  
Feasibility Study ◽  
Patient Information ◽  
Surgical Procedure ◽  
Information Needs ◽  
Interactive Media ◽  
Empirical Evaluation ◽  
Multimedia Application ◽  
Design Review ◽  
Patient Information Needs ◽  
Potential Use

This paper describes the development and refinement of the ‘Interactive GuideTM’, a multimedia application designed to explore the potential use of interactive media for communication/advertising. The domain selected for this empirical evaluation was radial keratotomy, a surgical procedure to correct nearsightedness. Starting from an analysis of patient information needs, we conducted iterative cycles of design, review, and testing which focused on topic selection, presentation styles and usability. Usage data and feedback from subjects have been very encouraging, pointing to the potential of this approach to establish a new style of information delivery.

scholarly journals Grid Map Merging with Ant Colony Optimization for Multi-Robot Systems

2021 ◽  
Author(s):  
Heoncheol Lee
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Working Environment ◽  
Grid Map ◽  
Localization And Mapping ◽  
Robot Systems ◽  
The Individual ◽  
Merging Algorithms ◽  
Multi Robot ◽  
Map Merging

Multi-robot systems have recently been in the spotlight in terms of efficiency in performing tasks. However, if there is no map in the working environment, each robot must perform SLAM which simultaneously performs localization and mapping the surrounding environments. To operate the multi-robot systems efficiently, the individual maps should be accurately merged into a collective map. If the initial correspondences among the robots are unknown or uncertain, the map merging task becomes challenging. This chapter presents a new approach to accurately conducting grid map merging with the Ant Colony Optimization (ACO) which is one of the well-known sampling-based optimization algorithms. The presented method was tested with one of the existing grid map merging algorithms and showed that the accuracy of grid map merging was improved by the ACO.

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