2019
DOI: 10.1177/1687814019884767
|View full text |Cite
|
Sign up to set email alerts
|

Vanishing point detection in corridor for autonomous mobile robots using monocular low-resolution fisheye vision

Abstract: It is crucial for mobile robots to implement vanishing point detection during navigation in corridors. For the fisheye vision, the conventional methods of vanishing point detection usually obtain poor detection results. This is mainly attributed to serious barrel distortion in images acquired from fisheye cameras that are widely used in mobile robot systems. In the proposed system, a novel vanishing point detection algorithm based on the Gabor filter bank and the convolutional neural network is put forward to … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 16 publications
0
1
0
Order By: Relevance
“…The system assumes that the yaw ϕ V I estimated by the visual-inertial SLAM in the same corridor remains within a certain range. That is when the change amount of ϕ V I in consecutive keyframes exceed the set threshold , the corridor frame is changed [37,42,43]. When a new corridor is detected, the last corridor's local optimization is stopped, and the current corridor's local optimization is continued.…”
Section: The Local Optimization Methods Based On the Sliding Windowmentioning
confidence: 99%
“…The system assumes that the yaw ϕ V I estimated by the visual-inertial SLAM in the same corridor remains within a certain range. That is when the change amount of ϕ V I in consecutive keyframes exceed the set threshold , the corridor frame is changed [37,42,43]. When a new corridor is detected, the last corridor's local optimization is stopped, and the current corridor's local optimization is continued.…”
Section: The Local Optimization Methods Based On the Sliding Windowmentioning
confidence: 99%