Warehouse aisles, airport concourses, tunnel sections, long corridors in logistics facilities. These environments share a property that makes localization structurally difficult: they are auto-similar. The geometry repeats at regular intervals. Any section looks like any other section from a local perspective, because any section was built the same way as every other section.
Feature-based localization depends on extracting distinctive landmarks from the environment and using them to establish position. In repetitive environments, most observable features are not distinctive. A rack support seen at position 50 m looks identical to the same rack support at position 150 m. A tunnel cross-section at kilometer 3 looks identical to the one at kilometer 8. The feature extractor finds matches, but those matches are equally consistent with many different positions in the map.
This is perceptual aliasing: the system believes it has correctly localized because the local observation is consistent with the map, while being in an entirely different location with geometrically identical structure. The error is undetectable from local evidence alone. The system has no way to know it is wrong based on what it can currently observe.
If your deployment involves repetitive geometry, we can show you directly how the localization performs in an environment with your specific structure before you commit to the system.
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