Why SLAM Is Harder Than It Looks

Simultaneous Localization and Mapping sounds like a textbook problem you solve once and reuse forever. In practice, the algorithm is the easy 20% — the hard 80% is sensor calibration, timing synchronization between IMU and LiDAR, and handling the moment your robot drives past a featureless white wall and loses track of where it is.

This post covers the failure modes I've personally hit — drift on long corridors, loop-closure false positives, and the specific IMU noise that ruined a full day of testing — and what actually fixed each one.