Title: Switchable Constraints vs. Max-Mixture Models vs. RRR - A Comparison of Three Approaches to Robust Pose Graph SLAM
Authors: Niko Sünderhauf and Peter Protzel
Abstract:
SLAM algorithms that can infer a correct map despite the presence of outliers have recently attracted increasing attention. In the context of SLAM, outlier constraints are typically caused by a failed place recognition due to perceptional aliasing. If not handled correctly, they can have catastrophic effects on the inferred map. Since robust robotic mapping and SLAM are among the key requirements for autonomous long-term operation, inference methods that can cope with such data association failures are a hot topic in current research. Our paper compares three very recently published approaches to robust pose graph SLAM, namely switchable constraints, max-mixture models and the RRR algorithm. All three methods were developed as extensions to existing factor graph-based SLAM back-ends and aim at improving the overall system’s robustness to false positive loop closure constraints. Due to the novelty of the three proposed algorithms, no direct comparison has been conducted so far.
IEEE International Conference on Robotics and Automation (ICRA), 2013
Link:
LocalLink
http://www.tu-chemnitz.de/etit/proaut/rsrc/ICRA12-comparisonRobustSLAM.pdf
Reference Link:
Switchable Constraints
http://www.tu-chemnitz.de/etit/proaut/mitarbeiter/rsrc/IROS12-switchableConstraints.pdf
Max-Mixture
http://www.roboticsproceedings.org/rss08/p40.pdf
RRR
http://www.roboticsproceedings.org/rss08/p30.pdf
Thursday, June 27, 2013
Monday, June 17, 2013
Lab Meeting Jun. 19, 2013 (Alan) : Dense Variational Reconstruction of Non-Rigid Surfaces from Monocular Video
Title: Dense Variational Reconstruction of Non-Rigid Surfaces from Monocular Video (CVPR 2013 Oral)
Authors: Ravi Garg, Anastasios Roussos, Lourdes Agapito
Abstract
This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate nonrigid structure from motion (NRSfM) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along with the camera motion matrices, given dense 2D correspondences.
Unlike traditional factorization based approaches to NRSfM, which model the low-rank non-rigid shape using a fixed number of basis shapes and corresponding coefficients, we minimize the rank of the matrix of time-varying shapes directly via trace norm minimization. In conjunction with this low-rank constraint, we use an edge preserving total-variation regularization term to obtain spatially smooth shapes for every frame. Thanks to proximal splitting techniques the optimization problem can be decomposed into many point-wise sub-problems and simple linear systems which can be easily solved on GPU hardware. We show results on real sequences of different objects (face, torso, beating heart) where, despite challenges in tracking, illumination changes and occlusions, our method reconstructs highly deforming smooth surfaces densely and accurately directly from video, without the need for any prior models or shape templates.
Link
Authors: Ravi Garg, Anastasios Roussos, Lourdes Agapito
Abstract
This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate nonrigid structure from motion (NRSfM) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along with the camera motion matrices, given dense 2D correspondences.
Unlike traditional factorization based approaches to NRSfM, which model the low-rank non-rigid shape using a fixed number of basis shapes and corresponding coefficients, we minimize the rank of the matrix of time-varying shapes directly via trace norm minimization. In conjunction with this low-rank constraint, we use an edge preserving total-variation regularization term to obtain spatially smooth shapes for every frame. Thanks to proximal splitting techniques the optimization problem can be decomposed into many point-wise sub-problems and simple linear systems which can be easily solved on GPU hardware. We show results on real sequences of different objects (face, torso, beating heart) where, despite challenges in tracking, illumination changes and occlusions, our method reconstructs highly deforming smooth surfaces densely and accurately directly from video, without the need for any prior models or shape templates.
Link
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