首页|Findings from University of California San Diego (UCSD) Broaden Understanding of Robotics (Multi-robot Object Slam Using Distributed Variational Inference)
Findings from University of California San Diego (UCSD) Broaden Understanding of Robotics (Multi-robot Object Slam Using Distributed Variational Inference)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news originating from San Diego, California, by NewsRx corr espondents, research stated, "Multi-robot simultaneous localization and mapping (SLAM) enables a robot team to achieve coordinated tasks by relying on a common map of the environment. Constructing a map by centralized processing of the robo t observations is undesirable because it creates a single point of failure and r equires pre-existing infrastructure and significant communication throughput." Funders for this research include NSF FRR CAREER, ARL DCIST CRA. Our news journalists obtained a quote from the research from the University of C alifornia San Diego (UCSD), "This letter formulates multi-robot object SLAM as a variational inference problem over a com-munication graph subject to consensus c onstraints on the object estimates maintained by different robots. To solve the problem, we develop a distributed mirror descent algorithm with regularization e nforcing consensus among the communicating robots. Using Gaussian distributions in the algorithm, we also derive a distributed multi-state constraint Kalman fil ter (MSCKF) for multi-robot object SLAM."
San DiegoCaliforniaUnited StatesNo rth and Central AmericaEmerging TechnologiesMachine LearningNano-robotRo botRoboticsUniversity of California San Diego (UCSD)
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