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

Abstract

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 to describe feature vectors in this model. First, in order to add some redundant image information, statistical information of the spatial relationships of all the feature points in an image, i.e. relative distances and angles, is used to extend the feature vectors in the original BoW model. Then, the support vector machine (SVM) classifier is used to classify objects. Also, in order to navigate conveniently in unknown and dynamic indoor environments, a type of human–robot interaction method based on a hand-drawn semantic map is considered. The experimental results show that this new navigation technology for indoor mobile robots is very robust and highly effective.

Keywords

• 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
• mobile robots
• extended bag
• words model
• general object recognition