Camera Sensor Model for Visual SLAM

Wu, Jing; Zhang, Hong

doi:10.1109/crv.2007.14

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…SLAM is essentially a state estimation problem where uncertainty is inherently present. Most existing work in the literature use a fixed model for describing camera noise distribution [137]. However, this might not always be the case in real-world applications due to many unpredictable factors such as occlusions, the sudden appearance of obstacles, and texture-less environments.…”

Section: Confidence Learningmentioning

confidence: 99%

Deep Learning Techniques for Visual SLAM: A Survey

et al. 2023

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Visual Simultaneous Localization and Mapping (VSLAM) has attracted considerable attention in recent years. This task involves using visual sensors to localize a robot while simultaneously constructing an internal representation of its environment. Traditional VSLAM methods involve the laborious hand-crafted design of visual features and complex geometric models. As a result, they are generally limited to simple environments with easily identifiable textures. Recent years, however, have witnessed the development of deep learning techniques for VSLAM. This is primarily due to their capability of modeling complex features of the environment in a completely data-driven manner. In this paper, we present a survey of relevant deep learning-based VSLAM methods and suggest a new taxonomy for the subject. We also discuss some of the current challenges and possible directions for this field of study.

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Section: Confidence Learningmentioning

confidence: 99%

Deep Learning Techniques for Visual SLAM: A Survey

et al. 2023

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“…Reliability of surveillance cameras are, in particular, of concern since their sensitivity is significantly compromised by the distance to target, ambient lighting and camera inherent properties. A metric for reliability measurement of color surveillance cameras for detecting correct color of an approaching target entity is investigated in [20]. In their experiment, they used an indoor small-scale environment experimental setup to collect different camera performance by varying the distance between a checkerboard and surveillance camera CRIM model, we conducted a similar experiment described in the section 4.…”

Section: Camera Reliability Impact Modulation (Crim)mentioning

confidence: 99%

Multi-layered context impact modulation for enhanced focus of attention of situational awareness in persistent surveillance systems

Shirkhodaie

Rababaah

2010

SPIE Proceedings

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This paper presents a Multi-Layered Context Impact Modulation (MCIM) technique for persistent surveillance systems (PSS) and discusses its layered architecture for different context modulations including: spatial, temporal, sensor reliability, human presence, and environmental modulations. This paper also presents a fusion model for enhancement of focus of attention at the common operation picture (COP). The fusion model combines all the impacts from the different MCIM layers onto one unified modulated map. To test and evaluate the performance of MCIM, several experiments were conducted to modulate interaction of humans and vehicles which exhibit various normal and suspicious behaviors. The experimental results show strength of this approach in correctly modulating different suspicious situations with higher degree of certainty.

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“…Jing Wu and Zhang (2007) show a camera modeling for visual SLAM. It aims at discovering how to model optical sensor uncertainty and how to develop probabilistic compounds of the model.…”

Section: Related Workmentioning

confidence: 99%

Straight-lines modelling using planar information for monocular SLAM

Santana

Medeiros

2012

International Journal of Applied Mathematics and Computer Science

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This work proposes a SLAM (Simultaneous Localization And Mapping) solution based on an Extended Kalman Filter (EKF) in order to enable a robot to navigate along the environment using information from odometry and pre-existing lines on the floor. These lines are recognized by a Hough transform and are mapped into world measurements using a homography matrix. The prediction phase of the EKF is developed using an odometry model of the robot, and the updating makes use of the line parameters in Kalman equations without any intermediate stage for calculating the distance or the position. We show two experiments (indoor and outdoor) dealing with a real robot in order to validate the project.

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Camera Sensor Model for Visual SLAM

Cited by 10 publications

References 13 publications

Deep Learning Techniques for Visual SLAM: A Survey

Deep Learning Techniques for Visual SLAM: A Survey

Multi-layered context impact modulation for enhanced focus of attention of situational awareness in persistent surveillance systems

Straight-lines modelling using planar information for monocular SLAM

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