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genpark-particle-filter-monte-carlo-localization-skill

mcp
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SUMMARY

GenPark AI Agent Skill - Sequential Importance Resampling (SIR) particle filter for non-linear, non-Gaussian Monte Carlo agent state tracking and localization.

README.md

GenPark Particle Filter Monte Carlo Localization Skill

Sequential Importance Resampling (SIR) particle filter for non-linear Monte Carlo state estimation and localization.

Discover more at GenPark and the GenPark MCP Catalog.

graph TD
    A[Particle Cloud {x_i, w_i}] --> B[Motion Update: Predict x_i += v + noise]
    B --> C[Observation Likelihood Weighting w_i = P(z|x_i)]
    C --> D[Weight Normalization]
    D --> E[Systematic Low-Variance Resampling]
    E --> F[New Equi-weighted Particle Cloud]

Features

  • Non-parametric Monte Carlo representation of arbitrary belief states.
  • Low-variance systematic resampling wheel algorithm.
  • Pure Python standard library.

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