Vision-Based Indoor Localization for Unmanned Aerial Vehicles

Lee, Jeong-Oog; Kang, Taesam; Lee, Keun-Hwan; Im, Sung Kyu; Park, Jungkeun

doi:10.1061/(asce)as.1943-5525.0000064

Cited by 26 publications

(7 citation statements)

References 9 publications

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“…In [21], the ways to achieve natural landmark-based localization using a vision system for the indoor navigation of an Unmanned Aerial Vehicle (UAV) were discussed. The system first extracted feature points from the image data, taken by a monocular camera, using the SIFT algorithm.…”

Section: Related Workmentioning

confidence: 99%

Graph-Based Image Matching for Indoor Localization

Manzo

2019

MAKE

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Graphs are a very useful framework for representing information. In general, these data structures are used in different application domains where data of interest are described in terms of local and spatial relations. In this context, the aim is to propose an alternative graph-based image representation. An image is encoded by a Region Adjacency Graph (RAG), based on Multicolored Neighborhood (MCN) clustering. This representation is integrated into a Content-Based Image Retrieval (CBIR) system, designed for the vision-based positioning task. The image matching phase, in the CBIR system, is managed with an approach of attributed graph matching, named the extended-VF algorithm. Evaluated in a context of indoor localization, the proposed system reports remarkable performance.

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Section: Related Workmentioning

confidence: 99%

Graph-Based Image Matching for Indoor Localization

Manzo

2019

MAKE

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“…In (Lee et al, 2010) the method first extracts feature points from the image data taken by a monocular camera using the SIFT algorithm. The system selects landmark feature points that have distinct descriptor vectors among the feature points, calculate those points location and store them in a database.…”

Section: Related Workmentioning

confidence: 99%

Uav Visual Autolocalizaton Based on Automatic Landmark Recognition

Filho¹,

Shiguemori²,

Saotome

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Deploying an autonomous unmanned aerial vehicle in GPS-denied areas is a highly discussed problem in the scientific community. There are several approaches being developed, but the main strategies yet considered are computer vision based navigation systems. This work presents a new real-time computer-vision position estimator for UAV navigation. The estimator uses images captured during flight to recognize specific, well-known, landmarks in order to estimate the latitude and longitude of the aircraft. The method was tested in a simulated environment, using a dataset of real aerial images obtained in previous flights, with synchronized images, GPS and IMU data. The estimated position in each landmark recognition was compatible with the GPS data, stating that the developed method can be used as an alternative navigation system.

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“…Earlier research worked on the development of vision-based localization and map building for small UAVs. In previous research by the authors (Lee et al, 2010), we attempted to achieve natural landmark-based localization using a vision system for indoor navigation by small UAVs. Our method first extracts feature points (landmarks) from the image data taken by a monocular camera using the SIFT algorithm.…”

Section: Vision-based Autonomous Navigation Of Uavsmentioning

confidence: 99%

Obstacle avoidance for small UAVs using monocular vision

Lee

Park

et al. 2011

Aircraft Engineering and Aerospace Technology

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Purpose -The purpose of this paper is to propose that the three-dimensional information of obstacles should be identified to allow unmanned aerial vehicles (UAVs) to detect and avoid obstacles existing in their flight path. Design/methodology/approach -First, the approximate outline of obstacles was detected using multi-scale-oriented patches (MOPS). At the same time, the spatial coordinates of feature points that exist in the internal outline of the obstacles were calculated through the scale-invariant feature transform (SIFT) algorithm. Finally, the results from MOPS and the results from the SIFT algorithm were merged to show the three-dimensional information of the obstacles. Findings -As the method proposed in this paper reconstructs only the approximate outline of obstacles, a quick calculation can be done. Moreover, as the outline information is combined through SIFT feature points, detailed three-dimensional information pertaining to the obstacles can be obtained. Practical implications -The proposed approach can be used efficiently in GPS-denied environments such as certain indoor environments. Originality/value -For the autonomous flight of small UAVs having a payload limit, this paper suggests a means of forming three-dimensional information about obstacles with images obtained from a monocular camera.

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Vision-Based Indoor Localization for Unmanned Aerial Vehicles

Cited by 26 publications

References 9 publications

Graph-Based Image Matching for Indoor Localization

Graph-Based Image Matching for Indoor Localization

Uav Visual Autolocalizaton Based on Automatic Landmark Recognition

Obstacle avoidance for small UAVs using monocular vision

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