Testing Vision-Based Sensors for Enclosed Underwater Environments When Applied to EKF SLAM

Cain, Chris; Leonessa, Alexander

doi:10.1115/dscc2012-movic2012-8747

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…The UGV is equipped with Mecanum wheels [28] which allow the vehicle to move in any direction without the constraints that are typical with many ground vehicles. The use of Mecanum wheels makes it difficult to use standard wheel encoders for localization purposes; to overcome this a downward facing camera is attached to the UGV which provides visual odometry data for localization using the approach described in [29]. Other sensors attached to the UGV include a digital compass to provide the global heading of the vehicle and a Hokuyo UTM-30LX LiDAR sensor, with reported accuracy of 0.1 to 10 m ± 30 mm and 10 to 30 m ± 50 mm, to provide range and bearing measurements to objects in the environment.…”

Section: Resultsmentioning

confidence: 99%

FastSLAM Using Compressed Occupancy Grids

Cain¹,

Leonessa

2016

Journal of Sensors

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Robotic vehicles working in unknown environments require the ability to determine their location while learning about obstacles located around them. In this paper a method of solving the SLAM problem that makes use of compressed occupancy grids is presented. The presented approach is an extension of the FastSLAM algorithm which stores a compressed form of the occupancy grid to reduce the amount of memory required to store the set of occupancy grids maintained by the particle filter. The performance of the algorithm is presented using experimental results obtained using a small inexpensive ground vehicle equipped with LiDAR, compass, and downward facing camera that provides the vehicle with visual odometry measurements. The presented results demonstrate that although with our approach the occupancy grid maintained by each particle uses only40%of the data needed to store the uncompressed occupancy grid, we can still achieve almost identical results to the approach where each particle filter stores the full occupancy grid.

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Section: Resultsmentioning

confidence: 99%

FastSLAM Using Compressed Occupancy Grids

Cain¹,

Leonessa

2016

Journal of Sensors

Self Cite

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“…Due to the limited space on underwater robotic platforms, it is necessary to minimize the resource consumption of underwater visual SLAM to enhance its practicality [91]. Aiming to reduce the cost of robot localization in underwater confined environments, C. Cain and A. Leonessa [92] developed a localization platform that combines a downward camera and a visual rangefinder for visual SLAM.…”

Section: Visual Slam Localizationmentioning

confidence: 99%

A Review of Underwater Robot Localization in Confined Spaces

Wu,

Chen,

Yang

et al. 2024

JMSE

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Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.

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“…Due to the limited space on underwater robotic platforms, it is necessary to minimize the resource consumption of underwater visual SLAM to enhance its practicality [36]. C. Cain and A. Leonessa [37], aiming to reduce the cost of robot localization in underwater confined environments, developed a localization platform that combines a downward camera and a visual rangefinder for visual SLAM.…”

Section: Visual Slam Localizationmentioning

confidence: 99%

A Review of Underwater Robot Localization in Confined Spaces

Wu,

Chen,

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in underwater confined environments, researchers have studied different underwater positioning methods and selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms, and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have also developed some bio-inspired autonomous positioning methods. This article introduces the applicable robot positioning methods and sensors in underwater confined environments and discusses the research directions of robot positioning methods in such environments.

show abstract

Testing Vision-Based Sensors for Enclosed Underwater Environments When Applied to EKF SLAM

Cited by 3 publications

References 5 publications

FastSLAM Using Compressed Occupancy Grids

FastSLAM Using Compressed Occupancy Grids

A Review of Underwater Robot Localization in Confined Spaces

A Review of Underwater Robot Localization in Confined Spaces

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