Depth invariant visual servoing

Karasev, Peter; Serrano, Miguel M.; Vela, Patricio A.; Tannenbaum, Allen

doi:10.1109/cdc.2011.6161456

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…However, these techniques rely on the distinctiveness of features. For data association without distinct features, some techniques have been proposed based on point-set matching [11,19] or image registration [20]. These techniques are especially useful for cooperative cases in which the features from the marker pattern are all similar, such as fiducial dots.…”

Section: Related Workmentioning

confidence: 99%

Efficient Closed-Loop Detection and Pose Estimation for Vision-Only Relative Localization in Space with A Cooperative Target

Zhang

Vela

Tsiotras

et al. 2014

AIAA SPACE 2014 Conference and Exposition

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An integrated processing pipeline is presented for relative pose estimation of vision-only cooperative localization between two vehicles with unknown relative motion. The motivating scenario is that of proximity operations between two spacecraft when the target spacecraft has a special target pattern and the chase spacecraft is navigating using only a monocular visual sensor. The only prior information assumed is knowledge of the target pattern, which we propose to consist of nested circular blobs. The algorithm is useful for applications requiring localization accuracy using limited computational resources. It achieves low computational cost with high accuracy and robustness via the following contributions: (1) an adaptive visual pattern detection scheme based on the estimated relative pose, which improves both the efficiency of detection and accuracy of pose estimates; (2) a parametric blob detector called Box-LoG which is computationally efficient; and (3) an algorithm which jointly solves the frame-to-frame data association and relative pose estimation. An incremental smoothing technique temporally smooths the pose estimates. The approach can deal with target re-acquisition after loss of the target pattern from the field of view. The algorithm is tested in both synthetic simulations and on an actual spacecraft simulator platform.

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Section: Related Workmentioning

confidence: 99%

Efficient Closed-Loop Detection and Pose Estimation for Vision-Only Relative Localization in Space with A Cooperative Target

Zhang

Vela

Tsiotras

et al. 2014

AIAA SPACE 2014 Conference and Exposition

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“…It encapsulates the 3D information on Disptons 3 to compose jointly with the Textons 4 , a different set of features, to better represent the road class given by the complex environment. 1 Abbreviation for red, green and blue color space. 2 Abbreviation for hue, saturation and value color space.…”

Section: Road Recognitionmentioning

confidence: 99%

“…In recent years, several applications for control of autonomous vehicles and specialized Advanced Driving Assistance Systems (ADAS) were proposed [1] [2]. In most cases machine vision is used as a main source of information for road detection thanks to the facility to extract measurements related to texture and color.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive performance analysis of road detection algorithms using the common urban Kitti-road benchmark

Vitor

Victorino

Ferreira

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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The navigation of an autonomous vehicle is a highly complex task and the dynamic environment is used as a source for reasoning. Road detection is a major issue in autonomous systems and advanced driving assistance systems applied for inner-city. Uncertainty may arise in environments with unmarked or weakly marked roads or poor lightning conditions. Moreover, when a common benchmark is not used, it is hard to decide which approach performs better on the road detection problem. This paper introduces a comprehensive performance analysis of two road recognition approaches using the urban Kitti-road benchmark. The first approach makes the extraction of a feature set based on statistical measures of 2D and 3D information from each superpixel. An Artificial Neural Network is used to detect the road pattern. The second approach extracts the feature set based on a multi-normalized histogram of Textons and Disptons for each superpixel. This feature set is used as a source for a Joint Boosting algorithm to model the road pattern. The proposed work presents a detailed evaluation highliting the pros and cons of each approach.

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“…Where a and a * are the current and the desired areas of the shape. To choose visual features to control the translational Degrees Of Freedom (DOF), we can make use of the image moment point based features proposed in [18] and validated with more results in [19]. This major pitfall in the work of [10], as mentioned in the introduction, is the target's motion in the direction of the frontal axis of the center of the camera (z-axis), this can imply several singularities in their tracking.…”

Section: B Approach Descriptionmentioning

confidence: 99%

Image Based Visual Servoing for Multi Aerial Robots Formation

Bastourous

Al-Tuwayyij²,

Guérin

et al. 2020

2020 28th Mediterranean Conference on Control and Automation (MED)

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There are numerous advantages of flying in group over using single robot in mission execution. However this implies solving a crucial issue: the coordination between drones. Moreover, according to the targeted application, it may be necessary or desirable that drones fly following a given geometric shape (line, diamond, etc.), a problem known as formation control. Building and maintaining a spatial geometric shape while evolving within the environment usually requires extensive communications between the robots for coordinating their movements. In this work we focus on the use of an Image-Based Visual Servoing (IBVS) technique for building and maintaining a Leader-Follower (LF) configuration of multi aerial vehicles (UAVs) without communication. While most IBVS techniques either require rigor camera calibration or can not regulate the error according to the three robot axes, our approach avoids the calibration phase by relying on image moments features to provide a vision-based predictive compensation method. The follower robot's solution works in GNSS-denied conditions and can run using only on-board sensors. The method is validated through simulations for a group of three quadrotors.

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Depth invariant visual servoing

Cited by 5 publications

References 21 publications

Efficient Closed-Loop Detection and Pose Estimation for Vision-Only Relative Localization in Space with A Cooperative Target

Efficient Closed-Loop Detection and Pose Estimation for Vision-Only Relative Localization in Space with A Cooperative Target

Comprehensive performance analysis of road detection algorithms using the common urban Kitti-road benchmark

Image Based Visual Servoing for Multi Aerial Robots Formation

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