Vision-Based Global Localization Using Ceiling Space Density

Ribacki, Arthur; Jorge, Vitor A. M.; Mantelli, Mathias; Maffei, Renan; Prestes, Edson

doi:10.1109/icra.2018.8460515

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Then we have: tan (Φ − γ y ) = y tan ϕ+G y+F . We conclude that y = b 2 + a y tan ϕ+G y+F By setting y = y r + y c we obtain the landmark coordinate y c reported in (11).…”

Section: Appendix Amentioning

confidence: 85%

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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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“…Then we have: tan (Φ − γ y ) = y tan ϕ+G y+F . We conclude that y = b 2 + a y tan ϕ+G y+F By setting y = y r + y c we obtain the landmark coordinate y c reported in (11).…”

Section: Appendix Amentioning

confidence: 85%

“…Other Computer Vision based approaches are based on the Free Space Density concept. For example A. Ribacki et al use an upward facing camera to extract the ceiling boundaries for estimating the ceiling space density from the current image [11]. Other authors, for example [12,13] use the ceiling depth images for robot localization.…”

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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show abstract

“…Other Computer Vision based approaches are based on the Free Space Density concept. For example, A. Ribacki et al use an upward facing camera to detect the ceiling boundaries and to estimate the ceiling space density from the current image [26]. Other authors, for example [5,6] use the ceiling depth images for robot localization.…”

Section: Related Workmentioning

confidence: 99%

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

Cuzzocrea¹

2021

JVLC

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“…A wide variety of methods, such as LiDAR (Wang et al, 2020), RADAR (Han et al, 2019), inertial guidance (Gu et al, 2016; Song et al, 2022), vision (Marmol et al, 2019; Song et al, 2022), global navigation satellite system (GNSS) (Gu et al, 2016; Imperoli et al, 2018), and WiFi (Chen et al, 2017) have been utilized for self‐localization in a diverse range of robot or automatic systems which usually adopt multiple sorts of sensors for complementarity. These technologies support stable and reliable services in unpredictable environments of diverse robots: service robots such as robot vacuum cleaners, industrial robots, and autonomous vehicles (Costanzo et al, 2022; Kuutti et al, 2018; Ribacki et al, 2018). Consequently, they have had a substantial impact in bringing robotics technology from the laboratory to the market.…”

Section: Introductionmentioning

confidence: 99%

A compact RTK‐GNSS device for high‐precision localization of outdoor mobile robots

Lee,

Kim,

Yang

et al. 2024

Journal of Field Robotics

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Accurate location information is crucial for ensuring the reliability of service robots, such as field vehicles and agricultural robots. These practical robots often encounter fluctuating or inclined terrains in dynamic conditions during operation. Additionally, the performance of mobile robots depends on the physical size and weight of the localization system–physically heavy systems may degrade performance. In this paper, we present a compact, lightweight RTK‐GNSS device weighing under 40 g, which achieves centimeter‐level accuracy across different attitudes and dynamic situations. To enhance signal reception and positioning performance, we lessen noise levels in the power circuit and radio‐frequency signal conditioning using a low‐dropout regulator and low‐noise amplifier. Utilizing the configured device, we verified centimeter‐level accuracy through experiments of tracking an unmanned ground vehicle. The potential of the device is substantiated via three application scenarios—a localization of a moving human for a wearable device, a heading estimation of a mobile robot in stationary conditions, and practical field tests in various signal‐reception environments. Attributable to its compact dimensions and lightweight features, the proposed device can yield a low system strain while maintaining high accuracy and reliable performance, rendering it highly adaptable for various robotic applications.

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Vision-Based Global Localization Using Ceiling Space Density

Cited by 7 publications

References 5 publications

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

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

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

A compact RTK‐GNSS device for high‐precision localization of outdoor mobile robots

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