Most localization stacks are validated in good conditions and deployed in whatever conditions actually exist. Rain, fog, dust, direct sunlight, and temperature extremes are rarely part of the test protocol but are a guaranteed part of the operational reality for any outdoor deployment and many indoor industrial ones.
The characteristic that makes environmental conditions difficult is that they don't cause failures abruptly. A sensor that performs reliably at noon may produce observations that are less informative at dusk, in fog, or in an environment with high dust from ongoing operations. The degradation is gradual. The system continues running. The pose estimates continue being produced. But their quality is silently declining.
Camera-based systems are directly vulnerable to illumination. Changing light, reflections, strong backlight, and low contrast all reduce the quality of visual features. LiDAR is substantially more resilient to optical conditions, but not immune. Dense rain produces returns from water droplets that don't correspond to physical structure. Dust fills the measurement space with false reflections. In both cases the point cloud contains information that is not informative about the actual environment geometry and must be handled accordingly.
The deeper issue is that a system without observation quality monitoring cannot distinguish between operating normally and operating in degraded conditions. It continues producing confidence values and pose estimates that look normal from the outside while the quality of the underlying measurements has dropped. Errors accumulate without triggering any alert.
If your deployment involves outdoor operation or environments with variable industrial conditions, we can discuss what sensor robustness looks like in practice for your specific context.
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