Most localization systems carry an implicit assumption: the world is static. The map was built from a scan of the environment. Localization works by comparing new observations against that map. The assumption is that what is in the map is still there.
In controlled test environments this assumption holds well enough. In production it starts to fail on day one. Forklifts reposition pallets across the facility throughout each shift. People walk through scenes constantly. Vehicles enter and leave loading areas. Cranes move overhead in port environments. The environment is not static. It is continuously changing, and the rate of change scales with operational intensity.
The failure mode has two directions. In the first, moving objects are incorporated into the map during a mapping session. A forklift that was stationary during the capture becomes a permanent feature of the reference map. When that forklift moves, observations no longer match the map in that region, producing localization errors. In the second direction, moving objects observed at runtime are matched against static map features, creating incorrect correspondences that pull the pose estimate in the wrong direction.
Filtering moving objects is necessary but not sufficient. The harder problem is deciding in real time which parts of the current observation are stable enough to match against. Discarding every point that could be moving risks losing most of the scene in high-traffic environments. Keeping everything risks corrupting the match with dynamic content.
If your operating environment has significant dynamic traffic, the architecture of the localization system matters more than parameter tuning. We can tell you what to expect before deployment.
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