matlab-fuse-inertial-sensors

What problem does it solve?

This Skill helps you analyze sensor configurations and reliably create and tune inertial fusion filters in MATLAB Navigation Toolbox so your agent can estimate orientation or full pose from IMU/AHRS/INS data with the correct algorithm choice.

Core Features & Use Cases

• Filter selection from sensor availability: Chooses among imufilter, ahrsfilter, complementaryFilter, ecompass, ahrs10filter, insfilterMARG, insfilterAsync, insfilterNonholonomic, insfilterErrorState, insEKF, insCF based on whether you have gyro, magnetometer, altimeter, and GPS and whether you need real-time vs batch processing.
• End-to-end fusion workflow guidance: Covers construction options, correct sample-rate handling (SampleRate vs IMUSampleRate vs timestamp-driven), handling time gaps, and implementing predict/fuse loops or batch estimateStates workflows.
• Tuning and validation support: Guides correct tune signatures (in-place vs tunernoise+returned noise struct), tuner configuration, and practical output/pose extraction patterns (pose vs stateparts/estimateStates timetable).
• When NOT to use: Explicitly avoids vision-only SLAM and Simulink model-based fusion, and flags simulation/data-only workflows where a filter isn’t the right tool.

Quick Start

Use the matlab-fuse-inertial-sensors skill to generate MATLAB code that selects the best Navigation Toolbox inertial fusion filter for your specific IMU/magnetometer/GPS/altimeter setup and runs or batches the fusion loop to produce orientation (and optionally position/velocity) from your recorded sensor data.