Nonlinear factor recovery for long-term SLAM

@article{Mazuran2016NonlinearFR,
  title={Nonlinear factor recovery for long-term SLAM},
  author={Mladen Mazuran and Wolfram Burgard and Gian Diego Tipaldi},
  journal={The International Journal of Robotics Research},
  year={2016},
  volume={35},
  pages={50 - 72}
}
For long-term operations, graph-based simultaneous localization and mapping (SLAM) approaches require nodes to be marginalized in order to control the computational cost. In this paper, we present a method to recover a set of nonlinear factors that best represents the marginal distribution in terms of Kullback–Leibler divergence. The proposed method, which we call nonlinear factor recovery (NFR), estimates both the mean and the information matrix of the set of nonlinear factors, where the… 
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53 Citations
Highly Influential Citations
12
Background Citations
28
Methods Citations
23
Results Citations
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53 Citations

Approximating Marginalization with Sparse Global Priors for Sliding Window SLAM-Graphs

This work addresses the problem of computing landmark updates live in the vehicle, which requires efficient use of the computational resources, and employs a graph-based sliding window approach for simultaneous localization and incremental map refinement.

Factor descent optimization for sparsification in graph SLAM

Factor Descent is proposed, a new iterative optimization method to sparsify the dense result of node marginalization, which works by iterating factor by factor, which is compared with state-of-the-art methods in real world datasets with regards to the obtained solution and convergence rates.

Graph SLAM Sparsification With Populated Topologies Using Factor Descent Optimization

Factor descent and noncyclic factor descent are proposed, two simple algorithms for SLAM sparsification that match the state-of-art methods without any parameters to be tuned, and are compared against the state of the art with regard to accuracy and CPU time.

Decoupled, consistent node removal and edge sparsification for graph-based SLAM

This paper analytically shows that during marginalization, the correct choice of linearization points in constructing dense marginal factors is to use the relative (local), instead of global, state estimates in the Markov blanket of the marginalized node, which has lacked a general consensus in the literature.

Information Sparsification in Visual-Inertial Odometry

This paper presents a novel approach to tightly couple visual and inertial measurements in a fixed-lag visual-inertial odometry (VIO) framework using information sparsification, which sparsifies the dense prior with a nonlinear factor graph by minimizing the information loss.

A unified resource-constrained framework for graph SLAM

This work attempts to explicitly connect all of the aforementioned resource constraint requirements by considering the node removal and sparsification pipeline in its entirety, and produces node subset selection strategies that are optimal in terms of minimizing the impact of Kullback-Liebler divergence (KLD), of approximating the dense distribution by a sparse one.

A Partial Sparsification Scheme for Visual-Inertial Odometry

  • Z. ZhuWei Wang
  • Computer Science
    2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
  • 2020
The proposed scheme is to first retain the dense prior from the marginalization excluding visual measurements, followed by a dense marginalization step that connects landmarks, then partially sparsified to extract pseudo factors that maintain the overall sparsity while minimizing the information loss.

Toward Invariant Visual-Inertial State Estimation using Information Sparsification

This work presents a novel approach to tightly couple visual and inertial measurements in a fixed-lag VIO framework using information sparsification and provides detailed derivations of a novel IMU preintegration framework using the group affine properties.

Fast Nonlinear Approximation of Pose Graph Node Marginalization

A fast nonlinear approximation method for marginalizing out nodes on pose graphs for longterm simultaneous localization, mapping, and navigation by re-parameterizing from absolute-to relative-pose spaces, which eschews the expenses of many state-of-the-art convex optimization schemes.

Information sparsification for visual-inertial odometry by manipulating Bayes tree

...

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