Visual navigation method for indoor mobile robot based on extended BoW model

Visual navigation method for indoor mobile robot based on extended BoW model

This article proposes a new navigation method for mobile robots based on an extended bag of words (BoW) model for general object recognition in indoor environments. The scale-invariant feature transform (SIFT)-detection algorithm with the graphic processing unit (GPU) acceleration technology is used...

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Bibliographic Details
Main Authors: Xianghui Li, Xinde Li, Mohammad Omar Khyam, Chaomin Luo, Yingzi Tan
Format: Article
Language:English
Published: Wiley 2019-03-01
Series:CAAI Transactions on Intelligence Technology
Subjects:
feature extraction
mobile robots
transforms
human-robot interaction
object recognition
support vector machines
robot vision
scale-invariant feature
(SIFT)-detection algorithm
graphic processing unit acceleration technology
feature vectors
redundant image information
statistical information
feature points
relative distances
angles
original BoW model
support vector machine classifier
unknown environments
dynamic indoor environments
human–robot interaction method
navigation technology
indoor mobile robot
visual navigation method
extended BoW model
extended bag
words model
general object recognition
Online Access:https://digital-library.theiet.org/content/journals/10.1049/trit.2017.0020

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