Decentralized Extended Information Filter for Single-Beacon Cooperative Acoustic Navigation: Theory and Experiments

Webster, Sarah E.; Walls, Jeffrey M.; Whitcomb, Louis L.; Eustice, Ryan M.

doi:10.1109/tro.2013.2252857

Cited by 119 publications

(72 citation statements)

References 41 publications

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“…They later distributed this centralized filter result exactly [28], called the DEIF, by leveraging the sparse update properties of the delayed-state information filter. Their solution requires a strict server-to-client support topology, as the server transmits representative local information to the client where the centralized filter solution is reproduced.…”

Section: Related Workmentioning

confidence: 99%

“…Our work is closest to [28], [3], and [26] in its effort to distribute local data fusion in an optimal way and to leverage the sparsity of the Gaussian information form to compactly transmit this information. In this paper we (i) build upon the previously reported approach in [26] by improving network scalability via a passive origin shifting scheme that eliminates the need for acknowledgments, (ii) introduce a recovery packet mechanism that enables clients to enter and leave the network or recover after a long period of communication dropout, and (iii) present full-scale comparative AUV trials demonstrating our algorithm's ability for real-time underwater navigation.…”

Section: Related Workmentioning

confidence: 99%

“…The OSM algorithm uses the DEIF algorithm [28] update to fuse the client state estimate following OWTT range observations. The DEIF algorithm is a method in which a client vehicle can exactly reproduce the solution of a centralized filter for server-to-client cooperative networks.…”

Section: Decentralized Extended Information Filtermentioning

confidence: 99%

“…1) We present a general algorithm, called the origin state method (OSM), that allows multiple servers to transmit their pose-graphs via a faulty, low bandwidth, communication channel, and to optimally fuse this information onboard a client within a decentralized extended information filter (DEIF) [28] that works in real-time for practical, underwater, acoustic networks. 2) We present a comparative experimental evaluation in fullscale autonomous underwater vehicle (AUV) trials.…”

Section: Introductionmentioning

confidence: 99%

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An Exact Decentralized Cooperative Navigation Algorithm for Acoustically Networked Underwater Vehicles with Robustness to Faulty Communication: Theory and Experiment

Walls¹,

Eustice²

2013

Robotics: Science and Systems IX

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Abstract-This paper reports on an exact real-time solution for server-client cooperative localization over a faulty and extremely bandwidth-limited underwater communication channel. Our algorithm, termed the origin state method, enables a 'server' vehicle to aid the navigation of multiple 'client' vehicles via a novel representation of the server's pose-graph that is robust to communication packet loss. This transmitted pose-graph can be used in conjunction with a decentralized extended information filter on the client to reproduce the corresponding two-vehicle server-client centralized result exactly. We present a full comparative evaluation for the first-ever real-time field implementation of the proposed algorithm for a multi-agent autonomous underwater vehicle network using underwater acoustic modems to communicate in a synchronous-clock transmission framework.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Decentralized Extended Information Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Exact Decentralized Cooperative Navigation Algorithm for Acoustically Networked Underwater Vehicles with Robustness to Faulty Communication: Theory and Experiment

Walls¹,

Eustice²

2013

Robotics: Science and Systems IX

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“…• We use the OSM algorithm to compute a 'delta information' over server TOL poses, which serves as input to a decentralized extended information filter (DEIF) algorithm (Webster et al, 2013) that exactly computes the result of the centralized extended information filter (CEIF) onboard the client up to communication delay.…”

Section: Introductionmentioning

confidence: 99%

An origin state method for communication constrained cooperative localization with robustness to packet loss

Walls

Eustice

2014

The International Journal of Robotics Research

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This paper reports on an exact, real-time solution for server-client cooperative localization over a faulty and extremely bandwidth-limited underwater communication channel. Our algorithm, termed the origin state method, enables a 'server' vehicle to broadcast its navigation information to multiple 'client' vehicles over a bandwidthlimited and faulty communication channel. The server's broadcasted pose-graph can be used in conjunction with an estimator on the client to exactly reproduce the corresponding server-client centralized estimate. We present an evaluation over an extensive real-time field implementation of the proposed algorithm for a multi-agent autonomous underwater vehicle network using underwater acoustic modems to communicate in a synchronous-clock transmission framework.

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CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams

Schmuck

Chli

2018

Journal of Field Robotics

185

104

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Robotic collaboration promises increased robustness and efficiency of missions with great potential in applications, such as search‐and‐rescue and agriculture. Multiagent collaborative simultaneous localization and mapping (SLAM) is right at the core of enabling collaboration, such that each agent can colocalize in and build a map of the workspace. The key challenges at the heart of this problem, however, lie with robust communication, efficient data management, and effective sharing of information among the agents. To this end, here we present CCM‐SLAM, a centralized collaborative SLAM framework for robotic agents, each equipped with a monocular camera, a communication unit, and a small processing board. With each agent able to run visual odometry onboard, CCM‐SLAM ensures their autonomy as individuals, while a central server with potentially bigger computational capacity enables their collaboration by collecting all their experiences, merging and optimizing their maps, or disseminating information back to them, where appropriate. An in‐depth analysis on benchmarking datasets addresses the scalability and the robustness of CCM‐SLAM to information loss and communication delays commonly occurring during real missions. This reveals that in the worst case of communication loss, collaboration is affected, but not the autonomy of the agents. Finally, the practicality of the proposed framework is demonstrated with real flights of three small aircraft equipped with different sensors and computational capabilities onboard and a standard laptop as the server, collaboratively estimating their poses and the scene on the fly.

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Decentralized Extended Information Filter for Single-Beacon Cooperative Acoustic Navigation: Theory and Experiments

Cited by 119 publications

References 41 publications

An Exact Decentralized Cooperative Navigation Algorithm for Acoustically Networked Underwater Vehicles with Robustness to Faulty Communication: Theory and Experiment

An Exact Decentralized Cooperative Navigation Algorithm for Acoustically Networked Underwater Vehicles with Robustness to Faulty Communication: Theory and Experiment

An origin state method for communication constrained cooperative localization with robustness to packet loss

CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams

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