Reliable Graphs for SLAM

Khosoussi, Kasra; Giamou, Matthew; Sukhatme, Gaurav S.; Huang, Shoudong; Dissanayake, Gamini; How, Jonathan P.

doi:10.1177/0278364918823086

Cited by 56 publications

(93 citation statements)

References 75 publications

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“…where expectation is with respect to the anisotropic random graph model defined in Definition 2; see [19]. Khosoussi et al [19] then seek to maximize the following objective:…”

Section: ) Tree-connectivitymentioning

confidence: 99%

Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection

Tian¹,

Khosoussi²,

Giamou³

et al. 2018

Robotics: Science and Systems XIV

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Abstract-Inter-robot loop closure detection is a core problem in collaborative SLAM (CSLAM). Establishing inter-robot loop closures is a resource-demanding process, during which robots must consume a substantial amount of mission-critical resources (e.g., battery and bandwidth) to exchange sensory data. However, even with the most resource-efficient techniques, the resources available onboard may be insufficient for verifying every potential loop closure. This work addresses this critical challenge by proposing a resource-adaptive framework for distributed loop closure detection. We seek to maximize task-oriented objectives subject to a budget constraint on total data transmission. This problem is in general NP-hard. We approach this problem from different perspectives and leverage existing results on monotone submodular maximization to provide efficient approximation algorithms with performance guarantees. The proposed approach is extensively evaluated using the KITTI odometry benchmark dataset and synthetic Manhattan-like datasets.

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“…where expectation is with respect to the anisotropic random graph model defined in Definition 2; see [19]. Khosoussi et al [19] then seek to maximize the following objective:…”

Section: ) Tree-connectivitymentioning

confidence: 99%

Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection

Tian¹,

Khosoussi²,

Giamou³

et al. 2018

Robotics: Science and Systems XIV

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several key factors are concluded, for example, orientation noises, and graph topologies. With the assumption of isometric additive Gaussian noise, Khosoussi et al [42] established the connection between the Fisher information matrix (FIM) of the estimate and the graph complexity.…”

Section: Related Workmentioning

confidence: 99%

Sparse Pose Graph Optimization in Cycle Space

2021

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The state-of-the-art modern pose-graph optimization (PGO) systems are vertex based. In this context, the number of variables might be high, albeit the number of cycles in the graph (loop closures) is relatively low. For sparse problems particularly, the cycle space has a significantly smaller dimension than the number of vertices. By exploiting this observation, in this article, we propose an alternative solution to PGO that directly exploits the cycle space. We characterize the topology of the graph as a cycle matrix, and reparameterize the problem using relative poses, which are further constrained by a cycle basis of the graph. We show that by using a minimum cycle basis, the cycle-based approach has superior convergence properties against its vertex-based counterpart, in terms of convergence speed and convergence to the global minimum. For sparse graphs, our cycle-based approach is also more time efficient than the vertex-based. As an additional contribution of this work, we present an effective algorithm to compute the minimum cycle basis. Albeit known in computer science, we believe that this algorithm is not familiar to the robotics community. All the claims are validated by experiments on both standard benchmarks and simulated datasets. To foster the reproduction of the results, we provide a complete open-source C++ implementation 1 of our approach. Index Terms-Minimum cycle basis, pose graph optimization (PGO), special Euclidean group (SE(3)), simultaneous localization and mapping (SLAM). I. INTRODUCTION P OSE graph optimization (PGO) is a fundamental problem, which arises in various research disciplines, such as simultaneous localization and mapping (SLAM) [1]-[5], structure from motion [6]-[8], calibration of multicamera rig [9], and sensor network localization [10], [11].A pose graph is a graph whose vertices encode positions and orientations of 3-D poses, and whose edges represent spatial constraints between the connected vertices. Taking a graph-based SLAM system as an example, the system processes the raw measurements to construct local maps. These local maps are Manuscript

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“…The PGO researches include least squares solvers, 14,15 number of local minima, convex relaxations, Riemmannian optimization, 16 outlier detection, [17][18][19][20][21] separability, and graph topology. In this article, we focus on solving outliers in loop closure and least squares solves.…”

Section: Related Workmentioning

confidence: 99%

Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

Feng

Wang

Yang

2020

International Journal of Advanced Robotic Systems

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Pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universität München (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures; Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

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Reliable Graphs for SLAM

Cited by 56 publications

References 75 publications

Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection

Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection

Sparse Pose Graph Optimization in Cycle Space

Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

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