Data association for mobile robot navigation: a graph theoretic approach

Bailey, Tim; Nebot, E.; Rosenblatt, Julio K.; Durrant‐Whyte, Hugh

doi:10.1109/robot.2000.846406

Cited by 110 publications

(82 citation statements)

References 12 publications

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“…During the early 2000's, along with new understanding of using information rather than covariance representations, graph based description of SLAM became started becoming more popular, for example Bailey [12]. The pivotal factor graph representation was described by Kschischang et al [129] in 2001.…”

Section: The Factor Graph Representationmentioning

confidence: 99%

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Multi-modal and inertial sensor solutions for navigation-type factor graphs

Fourie¹

2017

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This thesis presents a sum-product inference algorithm for platform navigation called Multi-modal iSAM (incremental smoothing and mapping). Common Gaussianonly likelihoods are restrictive and require a complex front-end processes to deal with non-Gaussian measurements. Instead, our approach allows the front-end to defer ambiguities with non-Gaussian measurement models. We retain the acyclic Bayes tree (and incremental update strategy) from the predecessor iSAM2 maxproduct algorithm [Kaess et al., IJRR 2012]. The approach propagates continuous beliefs on the Bayes (Junction) tree, which is an efficient symbolic refactorization of the nonparametric factor graph, and asymptotically approximates the underlying Chapman-Kolmogorov equations. Our method tracks dominant modes in the marginal posteriors of all variables with minimal approximation error, while suppressing almost all low likelihood modes (in a non-permanent manner). Keeping with existing inertial navigation, we present a novel, continuous-time, retroactively calibrating inertial odometry residual function, using preintegration to seamlessly incorporate pure inertial sensor measurements into a factor graph. We centralize around a factor graph (with starved graph databases) to separate elements of the navigation into an ecosystem of processes. Practical examples are included, such as how to infer multi-modal marginal posterior belief estimates for ambiguous loop closures; raw beam-formed acoustic measurements; or conventional parametric likelihoods, and others.

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Section: The Factor Graph Representationmentioning

confidence: 99%

“…Using Mathematica, we perform the symbolic computations to find the actual message belief 12) which is shown in Fig. 6-9.…”

Section: Leaf Clique Marginalmentioning

confidence: 99%

Multi-modal and inertial sensor solutions for navigation-type factor graphs

Fourie¹

2017

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“…Other popular method is the Joint Compatibility Branch and Bound (JCBB) [18], which considers the compatibility of many associations simultaneously. The Combined Constraint Data Association [2] builds a graph where the nodes are individually compatible associations and the edges relate binary compatible assignments. Over this graph, a Maximal Common Subgraph problem is solved for finding the maximum clique in the graph.…”

Section: Introductionmentioning

confidence: 99%

Consistent data association in multi-robot systems with limited communications

Aragüés

Montijano

Sagüés

2010

Robotics: Science and Systems VI

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Abstract-In this paper we address the data association problem of features observed by a robot team with limited communications. At every time instant, each robot can only exchange data with a subset of the robots, its neighbors. Initially, each robot solves a local data association with each of its neighbors. After that, the robots execute the proposed algorithm to agree on a data association between all their local observations which is globally consistent. One inconsistency appears when chains of local associations give rise to two features from one robot being associated among them. The contribution of this work is the decentralized detection and resolution of these inconsistencies. We provide a fully decentralized solution to the problem. This solution does not rely on any particular communication topology. Every robot plays the same role, making the system robust to individual failures. Information is exchanged exclusively between neighbors. In a finite number of iterations, the algorithm finishes with a data association which is free of inconsistent associations.In the experiments, we show the performance of the algorithm under two scenarios. In the first one, we apply the resolution and detection algorithm for a set of stochastic visual maps. In the second, we solve the feature matching between a set of images taken by a robotic team.

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“…Several authors have reported solutions to the first location using geometrical information gathered by a 2D laser scanner [1], [2], [3]. Our contribution is to combine this laser geometrical information with vision because cameras gather rich photometric information that can solve geometrical ambiguities.…”

Section: Introductionmentioning

confidence: 99%

Relocation using laser and vision

Ortin

Neira

Montiel

2004

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

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Abstract-We present a method for solving the first location problem using 2D laser and vision. Our observation is a twodimensional laser scan combined with its corresponding image. The observation is segmented in textured vertical planes; each vertical plane contains geometrical information about its location given by the laser scan, plus the gray level image obtained by the camera. The rich plane texture allows a safe plane recognition and the computer vision geometry allows to compute the relative camera motion once two planes are recognized as correspondent.The proposed algorithm outperforms both laser-only or visiononly algorithms. This is shown in the experimental results where a map composed of 8 observations of a 20 × 3 meters corridor allows to successfully locate the robot(without any other prior) in 163 out of 192 initial robot locations.

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Data association for mobile robot navigation: a graph theoretic approach

Cited by 110 publications

References 12 publications

Multi-modal and inertial sensor solutions for navigation-type factor graphs

Multi-modal and inertial sensor solutions for navigation-type factor graphs

Consistent data association in multi-robot systems with limited communications

Relocation using laser and vision

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