Improving vision-based control using efficient second-order minimization techniques

By Ezio Malis. IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, 2004

319 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/532e3a3146fae19cf760dfe72bfb1210a0ccf71d

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

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Citations in the corpus

Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera (2011, methodology): “Refinement is computed by minimizing the sum-ofsquare errors of the descriptors, using ESM to solve the iterative nonlinear least squares problem [4].”
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques (2018, methodology): “The estimation of the interaction matrix is obtained as proposed in [31], where L + s = ( L s + L s ∗ ) + , being L s ∗ the interaction matrix at the desired feature location, and ( .”
Visual servo control. I. Basic approaches (2006, background): “Finally, the choice L̂+e = 1/2(Le + Le∗)+ has recently been proposed in [9].”
Real-time image-based tracking of planes using efficient second-order minimization (2004, methodology): “Thus, the efficient second-order minimization (ESM) proposed in [13] can be used to improve template-based visual tracking.”
Deformable Medical Image Registration: A Survey (2013, methodology): “In the demons registration setting, an extension of Gauss-Newton by Malis [43] was employed to derive the symmetric demons forces [47], [50].”
Photometric visual servoing for omnidirectional cameras (2013, methodology): “…images: The control law is then, as in a classical visual servoing scheme, given by: Nevertheless, considering visual servoing as an optimization problem (Malis 2004), Collewet and Marchand (2011) formulate photometric servoing control law using a Levenberg-Marquardt like optimization technique.”
Vision based navigation for micro helicopters (2012, methodology): “For rotation compensation we use efficient second-order minimization techniques (ESM) (Malis (2004)) in order to keep PTAM independent of IMU readings.”
Point-based and region-based image moments for visual servoing of planar objects (2005, background): “Indeed, it has been recently proved in [17] that this choice largely improves the system behavior.”
Diffeomorphic demons: Efficient non-parametric image registration (2009, methodology): “One of the main results of this theoretical analysis we initially presented in (Vercauteren et al., 2007a) is to show that the symmetric forces variant can be related to the ESM scheme of (Benhimane and Malis, 2004; Malis, 2004).”
Homography-based 2D Visual Tracking and Servoing (2007, methodology): “We used the ESM1 visual tracking algorithm (Benhimane and Malis 2004) to track the area of interest and at the same time to estimate the homography matrix H.”
Non-parametric Diffeomorphic Image Registration with the Demons Algorithm (2007, methodology): “We thus perform an optimization procedure on a Lie group such as in [10, 11].”
ViSP for visual servoing: a generic software platform with a wide class of robot control skills (2005, unknown): “• L̂s = 1 2 ( L̂s(s, r) + L̂s(s , r) ) as reported in [23].”
Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments (2012, methodology): “For rotation compensation we use efficient second-order minimization techniques (ESM) [16] in order to keep PTAM independent of IMU readings.”