Global mapping and localization for mobile robots using stereo vision

Hadda, Imed; Knani, Jilani

doi:10.1109/ssd.2013.6564130

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The registration process, that is, the matching between the local key-points and the 3D map key-points is performed with an original method described in [2]. Matching quality is evaluated using a dot product between the SIFT descriptor of the local key-points and the 3D map key-points.…”

Section: B Localization Using 3d Map and Local Key-pointsmentioning

confidence: 99%

Low cost 3D mapping for indoor navigation

Bergeon

Hadda

Křivánek

et al. 2015

International Conference on Military Technologies (ICMT) 2015

Self Cite

View full text Add to dashboard Cite

This article focuses on the ability to use a robot with a low cost 3D Scanner (Kinect) in an indoor environment to do a mapping of different rooms in a building and to be able to localize itself when going back to the same room. This method uses SIFT points (points of interest in images) to be able to reconstruct the environment in 3D and uses these SIFT points to identify the localization of the robot.

show abstract

Section: B Localization Using 3d Map and Local Key-pointsmentioning

confidence: 99%

Low cost 3D mapping for indoor navigation

Bergeon

Hadda

Křivánek

et al. 2015

International Conference on Military Technologies (ICMT) 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, this method cannot be used for self-positioning when there is no unevenness due to corners or columns in the wall form, because optimal matching becomes impossible. Measurement and matching using stereovision is an effective method in environments where walls have little unevenness [8][9][10][11][12][13][14][15][16][17]. This method uses a camera to capture images of the environment, and performs matching using object corners and patterns or large luminance gradients from lighting to register landmark points.…”

Section: Introductionmentioning

confidence: 99%

Improving landmark detection accuracy for self-localization through baseboard recognition

Premachandra

Murakami

Gohara

et al. 2016

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

Acquiring information related to the surrounding environment and environmental mapping are important issues for realizing autonomous robot movement in an unknown environment, and these processes require position estimations for self-localization. We have conducted previous research related to self-localization in indoor environments through baseboard recognition. In that method, a mobile robot performs self-localization using camera-acquired images, from which the intersection of baseboards and the vertical lines of attached doorframes are registered as landmarks. However, this method is limited in that self-localization cannot be performed in cases where baseboards are not partially available or where walls and baseboards are the same color. The present research aims at addressing these issues and implementing the method in an actual mobile robot. The implementing robot is allowed to move autonomously, performing mapping and self-localization in real time. Addressing the problem of same-colored walls requires identification of the border between the wall and the baseboard, so we propose an image processing method for identifying these lines. The problem of lack of baseboards is solved through supplemental use of odometry, which does not rely on visual characteristics. The proposed method is experimentally verified through use of our algorithm to recognize baseboards, perform self-localization, and create maps. The results verify that baseboards can be recognized even when they are the same color as walls. We furthermore verify that landmark-mapping performance when moving in hallways is improved over the previous version.

show abstract