robotics-slam-state-estimation

What problem does it solve?

It helps you systematically verify that a SLAM or robot state-estimation paper’s modeling, notation, and experimental claims are internally consistent and technically defensible.

Core Features & Use Cases

• Technical Audit for State Estimation: Cross-checks the stated state vector, propagation model, residuals, Jacobians, and observability assumptions to catch model inconsistencies before polishing.
• Consistency Checks for Frames, Time, and Perturbations: Ensures frame conventions, gravity conventions, transform directions, covariance frames, and timestamp conventions match across text, figures, and equations.
• Experiment & Writing Guidance: Validates dataset/baseline alignment, insists on measurable map-quality and failure-analysis evidence, and promotes precise terminology for robotics audiences.

Quick Start

Use the robotics-slam-state-estimation skill to audit a SLAM manuscript by checking states, measurement models, frame/time conventions, backend design, degeneracy cases, metrics, baselines, and the clarity of writing.