How can developers manage false positives when performing plane detection in cluttered physical spaces?

Example Concept: Plane detection algorithms often rely on spatial mapping data to identify flat surfaces. In cluttered environments, false positives can be reduced by increasing the confidence threshold for plane detection, using temporal filtering to confirm persistent planes over time, and excluding small or irregularly shaped planes that are less likely to be useful. Additionally, developers can employ machine learning models trained to distinguish between actual planes and clutter-induced artifacts.