TY - GEN
T1 - SUPERVISED AND UNSUPERVISED DEEP LEARNING APPLICATIONS FOR VISUAL SLAM
T2 - ASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022
AU - Ukaegbu, Uchechi Faithful
AU - Tartibu, Lagouge Kwanda
AU - Lim, C. W.
N1 - Publisher Copyright:
Copyright © 2022 by ASME.
PY - 2022
Y1 - 2022
N2 - Visual Simultaneous Localization and Mapping (V-SLAM) is a trending robotics research concept as well as the basis for autonomous and smart navigation. It is an integral part of vision-based applications which include virtual reality, unmanned aerial vehicles, augmented reality, and unmanned ground vehicles. V-SLAM carries out localization and mapping by learning relevant feature points from images and estimating their pose based on the correlation between the camera and the feature points. It also represents the ability of a robot to effectively navigate itself, employing visual sensors and prior information of the given location, in an uncharted environment while updating and constructing a coordinated map of the scene. However, due to the challenges of data association triggered by illumination, different viewpoints and environment dynamics, there has been rapid adoption of deep learning in the area of feature extraction/description, pose/depth estimation, mapping, loop closure detection and global optimization as it concerns visual SLAM. This paper sets out to elucidate diverse applications of supervised and unsupervised deep learning methods in all aspects of visual SLAM. It also briefly explains a case study regarding the application of both deep learning and SLAM for underground mining applications. It highlights recent research developments in addition to limitations hindering their effective application and investigates how a combination of deep learning with other methods offers a promising direction for visual SLAM research.
AB - Visual Simultaneous Localization and Mapping (V-SLAM) is a trending robotics research concept as well as the basis for autonomous and smart navigation. It is an integral part of vision-based applications which include virtual reality, unmanned aerial vehicles, augmented reality, and unmanned ground vehicles. V-SLAM carries out localization and mapping by learning relevant feature points from images and estimating their pose based on the correlation between the camera and the feature points. It also represents the ability of a robot to effectively navigate itself, employing visual sensors and prior information of the given location, in an uncharted environment while updating and constructing a coordinated map of the scene. However, due to the challenges of data association triggered by illumination, different viewpoints and environment dynamics, there has been rapid adoption of deep learning in the area of feature extraction/description, pose/depth estimation, mapping, loop closure detection and global optimization as it concerns visual SLAM. This paper sets out to elucidate diverse applications of supervised and unsupervised deep learning methods in all aspects of visual SLAM. It also briefly explains a case study regarding the application of both deep learning and SLAM for underground mining applications. It highlights recent research developments in addition to limitations hindering their effective application and investigates how a combination of deep learning with other methods offers a promising direction for visual SLAM research.
KW - Autonomous Systems
KW - Deep learning
KW - Underground Mining
KW - Visual SLAM
UR - http://www.scopus.com/inward/record.url?scp=85148445907&partnerID=8YFLogxK
U2 - 10.1115/IMECE2022-95685
DO - 10.1115/IMECE2022-95685
M3 - Conference contribution
AN - SCOPUS:85148445907
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - Advanced Materials
PB - American Society of Mechanical Engineers (ASME)
Y2 - 30 October 2022 through 3 November 2022
ER -