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Sökning: id:"swepub:oai:DiVA.org:miun-43829" > Semi-Autonomous Nav...

Semi-Autonomous Navigation of Powered Wheelchairs : 2D/3D Sensing and Positioning Methods

Vilar, Cristian (författare)
Mittuniversitetet,Institutionen för elektronikkonstruktion,IoT-system
O'Nils, Mattias, 1969- (preses)
Mittuniversitetet,Institutionen för elektronikkonstruktion
Krug, Silvia (preses)
Mittuniversitetet,Institutionen för elektronikkonstruktion
visa fler...
Ragot, Nicolas, Associate professor (opponent)
ESIGELEC
visa färre...
 (creator_code:org_t)
ISBN 9789189341326
Sundsvall : Mid Sweden University, 2021
Engelska 64 s.
Serie: Mid Sweden University doctoral thesis, 1652-893X
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)
Abstract Ämnesord
Stäng  
  • Autonomous driving and assistance systems have become a reality for the automotive industry to improve driving safety in the car. Hence, the cars use a variety of sensors, cameras and image processing techniques to measure their surroundings and control their direction, braking and speed for obstacle avoidance or autonomously driving applications.Like the automotive industry, powered wheelchairs also require safety systems to ensure their operation, especially when the user has controlling limitations, but also to develop new applications to improve its usability. One of the applications is focused on developing a new contactless control of a powered wheelchair using the position of a caregiver beside it as a control reference. Contactless control can prevent control errors, but it can also provide better and more equal communication between the wheelchair user and the caregiverThis thesis evaluates the camera requirements for a contactless powered wheelchair control and the 2D/3D image processing techniques for caregiver recognition and position measurement beside the powered wheelchair. The research evaluates the strength and limitations of different depth camera technologies for caregiver feet detection above the ground plane to select the proper camera for the application. Then, a hand-crafted 3D object descriptor is evaluated for caregiver feet recognition and compared with respect to a state-of-the-art deep learning object detector. Results for both methods are good, however, the hand-crafted descriptor suffers from segmentation errors and consequently, their accuracy is lower. After the depth camera and image processing techniques evaluation, results show that it is possible to use only an RGB camera to recognize and measure his or her relative position.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Nyckelord

3D object recognition
YOLO
YOLO-Tiny
3DHOG
Histogram-of-Oriented-Gradients
ModelNet40
Feature descriptor
Intel RealSense
Depth camera
Wheelchair

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