Automatic Labeling of Natural Landmarks for Wheelchair Motion Planning

Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection of natura...

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Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 14; p. 3093
Main Authors Ngo, Ba-Viet, Nguyen, Thanh-Hai, Vu, Chi Cuong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Algorithms
Cameras
Coders
Color imagery
Control algorithms
Deep learning
Disabled persons
Feature extraction
Global positioning systems
GPS
Image processing
Indoor environments
Kinematics
Labeling
Localization
Methods
Motion planning
Object recognition
Proposals
Robots
Sensors
Technology application
Wheelchairs
Vietnam
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Summary:Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection of natural landmarks in RGBD images for navigation of mobile platforms in an indoor environment. This method can reduce the time for manually collecting and creating a dataset of landmarks. The wheelchair, equipped with a camera system, is allowed to move along corridors to detect and label natural landmarks automatically. These landmarks contain the camera and wheelchair positions with the 3D coordinates when storing the labeled landmark. The feature density method is comprised of Oriented FAST and Rotated BRIEF (ORB) feature extractors. Moreover, the central coordinates of the marked points in the obtained RGB images will be mapped to the images with the depth axis for determining the position of the RGB-D camera system in the spatial domain. An encoder and kinematics equations are applied to determine the position during movement. As expected, the system shows good results, such as a high IoU value of over 0.8 at a distance of less than 2 m and a fast time of 41.66 ms for object detection. This means that our technique is very effective for the automatic movement of the wheelchair.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12143093