scholarly journals Vision-based localization algorithm based on landmark matching, triangulation, reconstruction, and comparison

2005 ◽  
Vol 21 (2) ◽  
pp. 217-226 ◽  
Author(s):  
D.C.K. Yuen ◽  
B.A. MacDonald
Keyword(s):  
Localization Algorithm ◽  
Vision Based Localization ◽  
Landmark Matching

Egocentric Landmark-Based Indoor Guidance System for the Visually Impaired

2017 ◽  
Vol 8 (3) ◽  
pp. 55-69 ◽  
Author(s):  
Zhuorui Yang ◽  
Aura Ganz
Keyword(s):  
Visually Impaired ◽  
Indoor Environment ◽  
Field Of View ◽  
Guidance System ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Algorithm Performance ◽  
Illumination Changes ◽  
Low Illumination ◽  
Vision Based Localization

In this paper, we introduce an egocentric landmark-based guidance system that enables visually impaired users to interact with indoor environments. The user who wears Google Glasses will capture his surroundings within his field of view. Using this information, we provide the user an accurate landmark-based description of the environment including his relative distance and orientation to each landmark. To achieve this functionality, we developed a near real time accurate vision based localization algorithm. Since the users are visually impaired our algorithm accounts for captured images using Google Glasses that have severe blurriness, motion blurriness, low illumination intensity and crowd obstruction. We tested the algorithm performance in a 12,000 ft2 open indoor environment. When we have mint query images our algorithm obtains mean location accuracy within 5ft., mean orientation accuracy less than 2 degrees and reliability above 88%. After applying deformation effects to the query images such blurriness, motion blurriness and illumination changes, we observe that the reliability is above 75%.

Egocentric Landmark-Based Indoor Guidance System for the Visually Impaired

2018 ◽  
pp. 1483-1499
Author(s):  
Zhuorui Yang ◽  
Aura Ganz
Keyword(s):  
Visually Impaired ◽  
Illumination Intensity ◽  
Guidance System ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Algorithm Performance ◽  
Location Accuracy ◽  
Illumination Changes ◽  
Low Illumination ◽  
Vision Based Localization

In this paper, we introduce an egocentric landmark-based guidance system that enables visually impaired users to interact with indoor environments. The user who wears Google Glasses will capture his surroundings within his field of view. Using this information, we provide the user an accurate landmark-based description of the environment including his relative distance and orientation to each landmark. To achieve this functionality, we developed a near real time accurate vision based localization algorithm. Since the users are visually impaired our algorithm accounts for captured images using Google Glasses that have severe blurriness, motion blurriness, low illumination intensity and crowd obstruction. We tested the algorithm performance in a 12,000 ft2 open indoor environment. When we have mint query images our algorithm obtains mean location accuracy within 5ft., mean orientation accuracy less than 2 degrees and reliability above 88%. After applying deformation effects to the query images such blurriness, motion blurriness and illumination changes, we observe that the reliability is above 75%.

An Efficient Area-Based Localization Algorithm for Wireless Sensor Networks

2015 ◽  
Vol 10 (10) ◽  
pp. 1062
Author(s):  
A. Mesmoudi ◽  
Mohammed Feham ◽  
Nabila Labraoui ◽  
Chakib Bekara
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Localization Algorithm

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

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