Active Structure-from-Motion for 3d Straight Lines

Mateus, André; Tahri, Omar; Miraldo, Pedro

doi:10.1109/iros.2018.8593793

Cited by 13 publications

(13 citation statements)

References 37 publications

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“…From the dynamics of a 3D line in a camera's reference frame in [4] (Sec. II-A), one can conclude that the system is in the desired class of systems, after a simple coordinate change as shown in our previous work [20] (Sec. II-B).…”

Section: Introductionsupporting

confidence: 71%

Active Estimation of 3D Lines in Spherical Coordinates

Mateus

Tahri

Miraldo

2019

2019 American Control Conference (ACC)

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Straight lines are common features in human made environments, which makes them a frequently explored feature for control applications. Many control schemes, like Visual Servoing, require the 3D parameters of the features to be estimated. In order to obtain the 3D structure of lines, a nonlinear observer is proposed. However, to guarantee convergence, the dynamical system must be coupled with an algebraic equation. This is achieved by using spherical coordinates to represent the line's moment vector, and a change of basis, which allows to introduce the algebraic constraint directly on the system's dynamics. Finally, a control law that attempts to optimize the convergence behavior of the observer is presented. The approach is validated in simulation, and with a real robotic platform with a camera onboard.

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Section: Introductionsupporting

confidence: 71%

Active Estimation of 3D Lines in Spherical Coordinates

Mateus

Tahri

Miraldo

2019

2019 American Control Conference (ACC)

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“…A common methodology consists on imposing a desired transient response to the estimation error by acting on the camera linear velocity as addressed in [7]. This idea is extended to include other geometrical primitives such as lines [8], spherical and cylindrical targets [9] and moments [21]. Generally, when dealing with the SfM problem, a precise prior knowledge of the camera velocity is required to recover the 3D structure of the viewed scene.…”

Section: Introductionmentioning

confidence: 99%

Towards Kinematics From Motion: Unknown Input Observer and Dynamic Extension Approach

Benyoucef

Hadj-Abdelkader

Nehaoua

et al. 2022

IEEE Control Syst. Lett.

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This paper addresses an unknown input observer design to estimate simultaneously the 3D depth of a tracked image feature and the camera linear velocity using a low cost monocular camera and inertial sensor. The camera kinematic model is at first, augmented via the dynamic extension approach then described as a quasi-Linear Parameter Varying (qLPV) model. Further, the qLPV system is transformed into Takagi-Sugeno (T-S) form with unmeasured premise variables. The error convergence analysis is performed based on Lyapunov theory and Input to State Stability (ISS) property to ensure the boundedness of the state estimation error. Gains that guarantee the asymptotic stability of the estimation error can be properly computed by means of Linear Matrix Inequalities (LMIs). Finally the proposed approach is validated using synthetic data.

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“…Methods have been developed to reconstruct the 3D model of an object from a perspective camera [ 10 , 11 ]. Other features such as lines [ 12 ], cylinders and spheres [ 13 ], or planes [ 14 , 15 ] are also applied to a structure-from-motion (SfM) task. However, the aforementioned SfM methods rely on triangulation to recover the depth, and extension of these works to the motion estimation of moving targets is still challenging.…”

Section: Introductionmentioning

confidence: 99%

Visual Servoing of a Moving Target by an Unmanned Aerial Vehicle

Chen

Hung

Yang

et al. 2021

Sensors

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To track moving targets undergoing unknown translational and rotational motions, a tracking controller is developed for unmanned aerial vehicles (UAVs). The main challenges are to control both the relative position and orientation between the target and the UAV to within desired values, and to guarantee that the generated control input to the UAV is feasible (i.e., below its motion capability). Moreover, the UAV is controlled to ensure that the target always remains within the field of view of the onboard camera. These control objectives were achieved by developing a nonlinear-model predictive controller, in which the future motion of the target is predicted by quadratic programming (QP). Since constraints of the feature vector and the control input are considered when solving the optimal control problem, the control inputs can be bounded and the target can remain inside the image. Three simulations were performed to compare the efficacy and performance of the developed controller with a traditional image-based visual servoing controller.

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Active Structure-from-Motion for 3d Straight Lines

Cited by 13 publications

References 37 publications

Active Estimation of 3D Lines in Spherical Coordinates

Active Estimation of 3D Lines in Spherical Coordinates

Towards Kinematics From Motion: Unknown Input Observer and Dynamic Extension Approach

Visual Servoing of a Moving Target by an Unmanned Aerial Vehicle

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