Distributed consensus algorithms for merging feature-based maps with limited communication

Aragüés, Rosario; Cortés, Jorge; Sagüés, Carlos

doi:10.1016/j.robot.2011.01.002

Cited by 37 publications

(28 citation statements)

References 32 publications

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“…The contributions of this manuscript, and novelty with respect to our previous works on distributed map merging [23], [24] are the following: (i) the proposal of the dynamic consensus strategy where, at each step, a discrete-time version of the PI algorithm is executed; (ii) the careful study of the convergence rate of the dynamic consensus strategy; (iii) the applications of this study to characterize the errors in the map merging and understand the trade-offs between the number of iterations and the performance of the algorithm; (iv) the theoretical and experimental study of its time and communication complexity; and (v) the implementation for featurebased maps taking into account the possibly different features discovered by each robot during the exploration. In [23], our robots performed the exploration of the environment, and only at the end of it, ran a static consensus algorithm to merge their maps.…”

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confidence: 99%

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Distributed Consensus on Robot Networks for Dynamically Merging Feature-Based Maps

2012

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Abstract-We study the feature-based map merging problem in robot networks. Along its operation, each robot observes the environment and builds and maintains a local map. Simultaneously, each robot communicates and computes the global map of the environment. The communication between the robots is rangelimited. We propose a dynamic strategy based on consensus algorithms that is fully distributed and does not rely on any particular communication topology. Under mild connectivity conditions on the communication graph, our merging algorithm asymptotically converges to the global map. We present a formal analysis of its convergence rate and provide accurate characterizations of the errors as a function of the timestep. The proposed approach has been experimentally validated using real visual information.

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confidence: 99%

“…In [23], our robots performed the exploration of the environment, and only at the end of it, ran a static consensus algorithm to merge their maps. In this paper, instead, robots dynamically merge the information online, i.e., at the same time that they are performing the exploration.…”

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Distributed Consensus on Robot Networks for Dynamically Merging Feature-Based Maps

2012

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“…Assuming that the observed noise distribution is zero-mean Gaussian, then, the equation (24) can be computed in Gaussian representation shown on equation (24).…”

Section: F Consensus-based Calculation Of Particles Weight In Distrimentioning

confidence: 99%

Distributed Multi - Robot Rao - Blac k wellized Particle Filt er ing Simultaneous Localization and Mapping with Consensus Calculation of Particle Weight and Posterior Parameters

Hidayat¹,

Trilaksono²,

Hindersyah³

2017

ijeei

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Extensive research on single-robot simultaneous localization and mapping (SLAM) over the past decade has provided good estimation results in mapping small environment. This raises the idea for building a larger map by using some robots that are assembled within the team, also called multi-robot SLAM. There are two types of multirobot SLAM, that is, centralized and distributed multi-robot SLAM. Centralized multirobot SLAM method still has problems in case of system failure on some robots, especially the system failure on the robot central. Distributed multi-robot SLAM was developed to overcome the weaknesses in centralized multi-robot SLAM by: 1) building a map of larger environment by fusing local maps from a group of robots, 2) eliminating the dependence on central processing by using distributed computing, 3) overcoming the vulnerability in case of system failure in some robots, especially the system failure in the robot central, and 4) eliminating centralized communication, in which each robot requires only local communication with its neighboring robots. However, the distributed multirobot SLAM method has not yielded good results on map estimates and localization estimates. This paper proposes consensus-based distributed multi-robot SLAM method that can be applied to map common environments. We use FastSLAM algorithm as the main base in developing distributed multi-robot SLAM. We suggest using one of two selection of consensus parameters, that is, particle weight and posterior parameter. We assume that each robot has the same motion model and observation model; therefore, the system noise and observation noise is the same. The aim of this paper is to design consensus based distributed multi-robot SLAM method that provides better map estimation and localization results when applied to common environments. We concentrate to test the proposed methods on aspects: 1) root mean square error (RMSE) of map to see the map estimation performance, 2) RMSE of localization to see the robot's pose estimation performance, 3) mapping coverage to see how long it will take until all features are observed, and 4) processing time to see how long the computation process takes place per timestep.

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“…(3). In practice, robots can execute distributed data association methods [21], [22], [31] for feature-based maps to obtain these relationships. Robots discover new features in the information received from their neighbors, and introduce additional rows and columns in the information matrices and vectors for them.…”

Section: A Initial Correspondence and Data Associationmentioning

confidence: 99%

Feature-based map merging with dynamic consensus on information increments

2014

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Abstract-We study the problem of feature-based map merging in robot networks. Along its operation, each robot observes the environment and builds and maintains a local map. Simultaneously, each robot communicates and computes the global map of the environment. The communication between the robots is range-limited. We propose a dynamic strategy based on consensus algorithms that is fully distributed and does not rely on any particular communication topology. Robots reach consensus on the latest global map, using the map increments between the previous and the current time steps. Under mild connectivity conditions on the communication graph, our merging algorithm asymptotically converges to the global map. We give proofs of unbiasedness and consistency of this global map for all the robots, at each iteration. The proposed approach has been experimentally validated using real RGB-D images.

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Distributed consensus algorithms for merging feature-based maps with limited communication

Cited by 37 publications

References 32 publications

Distributed Consensus on Robot Networks for Dynamically Merging Feature-Based Maps

Distributed Consensus on Robot Networks for Dynamically Merging Feature-Based Maps

Distributed Multi - Robot Rao - Blac k wellized Particle Filt er ing Simultaneous Localization and Mapping with Consensus Calculation of Particle Weight and Posterior Parameters

Feature-based map merging with dynamic consensus on information increments

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