Graph SLAM Sparsification With Populated Topologies Using Factor Descent Optimization

Vallvé, Joan; Solà, Joan; Andrade‐Cetto, Juan

doi:10.1109/lra.2018.2798283

Cited by 26 publications

(18 citation statements)

References 18 publications

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“…Mazuran et al [20] go one step further, proposing nonlinear factor recovery that allows to replace dense factors by arbitrarily defined "virtual" nonlinear measurements. Vallvé et al [33] propose a factor descent algorithm for belief sparsification. The recent work of [12] does not aim for a sparsification of the global pose graph, but for an efficient sparse approximation of the dense prior resulting from marginalization of the past measurement in sliding-window SLAM approaches.…”

Section: Related Workmentioning

confidence: 99%

On the Redundancy Detection in Keyframe-Based SLAM

Schmuck

Chli

2019

2019 International Conference on 3D Vision (3DV)

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Egomotion and scene estimation is a key component in automating robot navigation, as well as in virtual reality applications for mobile phones or head-mounted displays. It is well known, however, that with long exploratory trajectories and multi-session mapping for long-term autonomy or collaborative applications, the maintenance of the everincreasing size of these maps quickly becomes a bottleneck. With the explosion of data resulting in increasing runtime of the optimization algorithms ensuring the accuracy of the Simultaneous Localization And Mapping (SLAM) estimates, the large quantity of collected experiences is imposing hard limits on the scalability of such techniques. Considering the keyframe-based paradigm of SLAM techniques, this paper investigates the redundancy inherent in SLAM maps, by quantifying the information of different experiences of the scene as encoded in keyframes. Here we propose and evaluate different information-theoretic and heuristic metrics to remove dispensable scene measurements with minimal impact on the accuracy of the SLAM estimates. Evaluating the proposed metrics in two state-of-the-art centralized collaborative SLAM systems, we provide our key insights into how to identify redundancy in keyframe-based SLAM.

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Section: Related Workmentioning

confidence: 99%

On the Redundancy Detection in Keyframe-Based SLAM

Schmuck

Chli

2019

2019 International Conference on 3D Vision (3DV)

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“…[Blanco et al 2008] used Bayesian filtering to provide a probabilistic estimation based on the reconstruction of the robot path in a hybrid discrete-continuous state space. [Vallvé et al 2018] proposed factor descent and non-cyclic factor descent, two simple algorithms for SLAM sparsification.…”

Section: Topological Slammentioning

confidence: 99%

Image-based camera localization: an overview

Tang

2018

Vis. Comput. Ind. Biomed. Art

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Recently, virtual reality, augmented reality, robotics, autonomous driving et al attract much attention of both academic and industrial community, in which image based camera localization is a key task. However, there has not been a complete review on image-based camera localization. It is urgent to map this topic to help people enter the field quickly. In this paper, an overview of image based camera localization is presented. A new and complete kind of classifications for image based camera localization is provided and the related techniques are introduced. Trends for the future development are also discussed. It will be useful to not only researchers but also engineers and other people interested.

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“…In [1,14] we introduced factor descent optimization. Given a non-dense factor topology, factor descent iteratively optimizes each of the factors leaving fixed the rest.…”

Section: Introductionmentioning

confidence: 99%

“…Its nature can be taken into account in the whole sparsification process. Indeed, the generalized formulation introduced in [1], and completed in [14], is suited to pose-graph SLAM. We found that in such case, some conditions can not take place and the general formulation can be simplified.…”

Section: Introductionmentioning

confidence: 99%

Pose-graph SLAM sparsification using factor descent

Vallvé

Solà

Andrade‐Cetto

2019

Robotics and Autonomous Systems

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Since state of the art simultaneous localization and mapping (SLAM) algorithms are not constant time, it is often necessary to reduce the problem size while keeping as much of the original graph's information content. In graph SLAM, the problem is reduced by removing nodes and rearranging factors. This is normally faced locally: after selecting a node to be removed, its Markov blanket sub-graph is isolated, the node is marginalized and its dense result is sparsified. The aim of sparsification is to compute an approximation of the dense and non-relinearizable result of node marginalization with a new set of factors. Sparsification consists on two processes: building the topology of new factors, and finding the optimal parameters that best approximate the original dense distribution. This best approximation can be obtained through minimization of the Kullback-Liebler divergence between the two distributions. Using simple topologies such as Chow-Liu trees, there is a closed form for the optimal solution. However, a tree is oftentimes too sparse and produces bad distribution approximations. On the contrary, more populated topologies require nonlinear iterative optimization. In the present paper, the particularities of pose-graph SLAM are exploited for designing new informative topologies and for applying the novel factor descent iterative optimization method for sparsification. Several experiments are provided comparing the proposed topology methods and factor descent optimization with state-of-the-art methods in synthetic and real datasets with regards to approximation accuracy and computational cost.

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Graph SLAM Sparsification With Populated Topologies Using Factor Descent Optimization

Cited by 26 publications

References 18 publications

On the Redundancy Detection in Keyframe-Based SLAM

On the Redundancy Detection in Keyframe-Based SLAM

Image-based camera localization: an overview

Pose-graph SLAM sparsification using factor descent

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