Most localization systems are designed and validated in environments that fit within a few hundred meters. The computational assumptions, the memory architecture, and the drift tolerances of these systems are calibrated for that scale. Extending to city-size deployments or large industrial sites (ports covering multiple square kilometers, rail networks spanning dozens of kilometers) introduces problems of a categorically different kind.
The first problem is data scale. A dense 3D map of a large environment contains significantly more data than a small one. Memory management, query efficiency, and the computational cost of searching for correspondences all become active constraints rather than non-issues. A system that loads its full reference map into memory works fine for a 500 m warehouse. It becomes impractical for a city district.
The second problem is drift accumulation. Small localization errors grow over long trajectories. A system with 0.1% linear drift error, which is acceptable for a 200 m run, produces 1 meter of accumulated error over a 1 km trajectory and tens of meters over a city-scale route.
The third problem is multi-session consistency. Large environments cannot always be captured in a single session. Survey vehicles may cover different sections on different days, with different weather conditions and potentially different sensor configurations. The resulting sub-maps must be merged into a globally consistent representation that doesn't carry seams or contradictions at the boundaries between sessions.
If your deployment area spans more than a few hundred meters, the scalability architecture of the mapping and localization system matters as much as its accuracy. We can walk through what large-scale deployment looks like in practice.
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