Simple and robust localization system using ceiling landmarks and infrared light

Vidal, Joel; Lin, Chyi‐Yeu

doi:10.1109/icca.2016.7505340

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…The infrared sensors consist of an infrared camera and an infrared LED array. A similar approach is described in [32] where artificial passive reflective landmarks are placed on the ceiling and an infrared camera plus an infrared LED source are used to capture the reflection the IR light on the landmark for estimating the robot pose.…”

Section: Related Workmentioning

confidence: 99%

Ceiling-Vision-Based Mobile Object Self-Localization: A Composite Framework

Cuzzocrea¹

2021

JVLC

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Section: Related Workmentioning

confidence: 99%

Ceiling-Vision-Based Mobile Object Self-Localization: A Composite Framework

Cuzzocrea¹

2021

JVLC

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“…which maps each pixel (i, j) of one image to the pixel (u, v) of the other image such that the difference between the two images is minimized, as shown in (9).…”

Section: Projective Transformationsmentioning

confidence: 99%

“…The infrared sensors consist of an infrared camera and an infrared LED array. A similar approach is described in [9] where artificial passive reflective landmarks are placed on the ceiling and an infrared camera plus an infrared LED source are used to capture the reflection the IR light on the landmark for estimating the robot pose. Wang et al describe in [18] a vision control system which capture ceiling RGB images with a camera placed on the robot, convert the image to HSV color space and use V channel images to reduce the effect of illumination lamps.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Monocular Mobile Object Self-localization Approach Based on Ceiling Vision

Cuzzocrea

Camilotti

Mumolo

2021

Lecture Notes in Networks and Systems

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This study deals with the estimation of the position of a mobile object using ceiling landmarks images acquired by a low resolution camera placed on a mobile object. The mobile object is moving in an indoor environment where light is given by electric lamps with circular holders. The images of the circular holders are projected on the image plane of the camera and are processed by means of computer vision algorithms. The pixels of the images of the light holders on the ceiling are mapped to the pixels of the images of the light holders on the image plane of the camera by means of a two dimensional dynamic programming algorithm (2D-DPA). The projection distortions are thus compensated and this reduces the estimation errors. The algorithm described in this paper estimates the distance from the camera lens to the center of the landmarks using only ceiling vision. Localization can be easily obtain from such distance estimations. The projections are geometrically described and the distance estimation is based on the pixels mapping information obtained by 2D-DPA.

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“…There are two types of landmarks, including natural and artificial landmarks. From geometric characteristics, the target images can be distinguished from the natural landmarks, and then its current location is calculated [13,14]. This method performs poorly in dynamic environments, and the processing could be more time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Automatic Labeling of Natural Landmarks for Wheelchair Motion Planning

Ngo

Hải

2023

Electronics

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Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection of natural landmarks in RGBD images for navigation of mobile platforms in an indoor environment. This method can reduce the time for manually collecting and creating a dataset of landmarks. The wheelchair, equipped with a camera system, is allowed to move along corridors to detect and label natural landmarks automatically. These landmarks contain the camera and wheelchair positions with the 3D coordinates when storing the labeled landmark. The feature density method is comprised of Oriented FAST and Rotated BRIEF (ORB) feature extractors. Moreover, the central coordinates of the marked points in the obtained RGB images will be mapped to the images with the depth axis for determining the position of the RGB-D camera system in the spatial domain. An encoder and kinematics equations are applied to determine the position during movement. As expected, the system shows good results, such as a high IoU value of over 0.8 at a distance of less than 2 m and a fast time of 41.66 ms for object detection. This means that our technique is very effective for the automatic movement of the wheelchair.

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Simple and robust localization system using ceiling landmarks and infrared light

Cited by 6 publications

References 6 publications

Ceiling-Vision-Based Mobile Object Self-Localization: A Composite Framework

Ceiling-Vision-Based Mobile Object Self-Localization: A Composite Framework

An Innovative Monocular Mobile Object Self-localization Approach Based on Ceiling Vision

Automatic Labeling of Natural Landmarks for Wheelchair Motion Planning

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