Scaling up Gaussian Belief Space Planning Through Covariance-Free Trajectory Optimization and Automatic Differentiation


Belief space planning provides a principled framework to compute motion plans that explicitly gather information from sensing, as necessary, to reduce uncertainty about the robot and the environment. We consider the problem of planning in Gaussian belief spaces, which are parameterized in terms of mean states and covariances describing the uncertainty. In… (More)
DOI: 10.1007/978-3-319-16595-0_30


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Citations per Year

Citation Velocity: 32

Averaging 32 citations per year over the last 3 years.

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