Fully distributed scalable smoothing and mapping with robust multi-robot data association

Cunningham, Alexander; Wurm, Kai M.; Burgard, Wolfram; Dellaert, Frank

doi:10.1109/icra.2012.6225356

Cited by 90 publications

(69 citation statements)

References 18 publications

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“…A decisionmaking architecture for a MRS -centralized or decentralized -has a requirement for information sharing because a robot in the team can only perceive local information. Effective data fusion may help us to produce an effective plan and then conduct multi-robot coordination [171] [172].  How to make humans to easily intervene MRSs according to what is needed?…”

Section: Resultsmentioning

confidence: 99%

A Survey and Analysis of Multi-Robot Coordination

Yan

Jouandeau

Chérif

2013

International Journal of Advanced Robotic Systems

530

279

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In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper.

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Section: Resultsmentioning

confidence: 99%

A Survey and Analysis of Multi-Robot Coordination

Yan

Jouandeau

Chérif

2013

International Journal of Advanced Robotic Systems

530

279

View full text Add to dashboard Cite

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“…Alternatively, information can be processed in the form of local maps, and these local maps can be kept independent by avoiding the introduction of global information into them; this is what we propose here, and it is also the approach followed in [21]. This strategy has the benefit that each robot can produce meaningful representations of the environment, which allows for several high level data association methods [21], [22]. Not introducing global data in the local maps, has the effect of keeping the local maps of different robots independent.…”

Section: Related Workmentioning

confidence: 99%

“…(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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“…Since our matching problem is closely related 3 to point-pattern matching, several other methods available in the literature can be used directly or with little adaptation, e.g., those by Thrun and Liu (2005); Cunningham et al (2012). In any case, the choice of the matching algorithm is essentially an implementation choice that does not affect the general working principle of the PMR algorithm to be described.…”

Section: A Binary Registrationmentioning

confidence: 99%

Mutual localization in multi-robot systems using anonymous relative measurements

Franchi

Oriolo

Stegagno

2013

The International Journal of Robotics Research

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Abstract-We propose a decentralized method to perform mutual localization in multi-robot systems using anonymous relative measurements, i.e., measurements that do not include the identity of the measured robot. This is a challenging and practically relevant operating scenario that has received little attention in the literature. Our mutual localization algorithm includes two main components: a probabilistic multiple registration stage, which provides all data associations that are consistent with the relative robot measurements and the current belief, and a dynamic filtering stage, which incorporates odometric data into the estimation process. The design of the proposed method proceeds from a detailed formal analysis of the implications of anonymity on the mutual localization problem. Experimental results on a team of differential-drive robots illustrate the effectiveness of the approach, and in particular its robustness against false positives and negatives that may affect the robot measurement process. We also provide an experimental comparison that shows how the proposed method outperforms more classical approaches that may be designed building on existing techniques. The source code of the proposed method is available within the MLAM ROS stack.

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Fully distributed scalable smoothing and mapping with robust multi-robot data association

Cited by 90 publications

References 18 publications

A Survey and Analysis of Multi-Robot Coordination

A Survey and Analysis of Multi-Robot Coordination

Feature-based map merging with dynamic consensus on information increments

Mutual localization in multi-robot systems using anonymous relative measurements

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