I'm curious, what algorithms do robot vacuums use to detect dust in the home? Do they rely on LiDAR mapping or cameras? How does their cleaning efficiency vary across different floor types (carpet, tile, etc.)? What do you think is the biggest limitation of these devices?
How do robot vacuum cleaning algorithms work?
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I also spent a whole week picking up crumbs from the floor by hand after getting my first robot vacuum. I even once sucked up the pet bowl with the vacuum, shortening the filter's lifespan 😅. Even though the algorithms keep improving, models that use LiDAR mapping still miss spots—especially on soft surfaces like carpets where the sensors slip. That's why I always end up hand-sweeping the same corners. The most annoying part is fine dust bypassing the vacuum and getting into the filter—nobody talks about that!
In models that use LiDAR for mapping, for example, the device measures and tracks the room's walls and objects with an accuracy of about 1-3 mm—it's very effective on flat surfaces like tiles, but I feel like carpets with their fibers and threads can sometimes slightly mislead the sensors.
If I were to answer your question, you'd see that most modern robot vacuums actually prefer hybrid LiDAR + camera systems. LiDAR is particularly precise for mapping rooms and avoiding obstacles, while cameras are used for more detailed texture differentiation (like stains on carpets). But you know Apple (yes, Apple has worked on robot vacuum projects) puts a lot of emphasis on sensor fusion in their approaches—they don’t just rely on LiDAR but also support it with IMU, ultrasonic, and even infrared sensors.
Now, what if the system is cleaning according to the room’s layout and suddenly encounters someone entering the house? How long does it take for the system to recognize that person as a "person" rather than an "obstacle" and re-optimize its cleaning path?