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Introduction
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============
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This package implements Multi-Modal iSAM (MM-iSAM) algorithm ([see references](http://www.juliarobotics.org/Caesar.jl/latest/refs/literature/)) which is focused towards hybrid non-parametric and parametric inference/state-estimation over large factor graphs. Inference is performed via the Bayes (junction) tree where non-parametric and parametric solutions are based on marginal-joint belief propagation (an approximate sum-product algorithm). Many benefits such as clique recycling is available. See the common Caesar.jl documenation for more details. Note, that IncrementalInference.jl **does not** have to be used with Caesar.jl, and aims to only implement the inference operations against mathematical abstractions such as Manifolds.jl.
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This package implements Multi-Modal iSAM (MM-iSAM) algorithm ([see references](http://www.juliarobotics.org/Caesar.jl/latest/refs/literature/)) which does hybrid non-parametric and parametric inference/state-estimation over large factor graphs. Inference is performed via the Bayes (junction) tree where non-parametric and parametric solutions are based on marginal-joint belief propagation (an approximate sum-product algorithm). Many benefits such as clique recycling is available. See the common Caesar.jl documenation for more details. Note, that IncrementalInference.jl **does not** have to be used with Caesar.jl, and aims to only implement the inference operations against mathematical abstractions such as Manifolds.jl.
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This package forms a cardinal piece of the [Caesar.jl](https://github.com/JuliaRobotics/Caesar.jl) robotics toolkit, including 3D visualization and database interaction, which can serve as a base station for a robotic platform. A standalone [Robot Motion Estimate](https://github.com/JuliaRobotics/RoME.jl) package extends the available variables, factors, and utilities for use in robotic navigation.
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