How do autonomous vacuum cleaners' cleaning algorithms work?

23 Ağu 11:45

The whole "cleaning algorithm" thing for robot vacuums actually starts with a LIDAR sensor doing a 360-degree scan, bro. Like, Roborock’s X30 or Xiaomi’s Gen 3 use LIDAR—they measure laser reflections off walls to map out the room. Some brands throw in camera sensors too—Dyson’s 360 Eye has a camera, but they use it to avoid obstacles instead of navigation, basically as a backup for LIDAR. So if you're wondering whether it’s all about 360 scanning or cameras, the answer is both: LIDAR handles navigation, while the camera captures finer details. When it comes to recognizing furniture, most algorithms rely on "object placement" modules. Modern vacuums, like Roborock’s system, use deep learning—it snaps a pic of the furniture, compares it to a cloud database, and goes, "That’s a chair, don’t go there." Some brands even use special sensors for object recognition, like detecting anomalies in the 3D map made by LIDAR to identify furniture. Older models just used ultrasonic sensors to measure distance and went, "There’s something here," but modern ones actually recognize what it is. Honestly, my take is that LIDAR is still the most reliable. Cameras can mess up—low light, glare, and suddenly the algorithm’s confused. Like, if there are curtains in a room or too much light coming through a window, the vacuum might go, "Is this a wall or a window?" So if you’re buying one, I’d say go for LIDAR-based, but if it also has a camera, that’s a nice bonus. What do you think? How do you see these systems?

23 Ağu 12:33

Wow man, I've got both the Xiaomi Mi Robot and Roborock S7 at home, and after checking them out thoroughly, I noticed some serious differences. For instance, Xiaomi models generally use LIDAR + camera navigation. While the LIDAR scans a room's map 360 degrees, the camera captures detailed images, helping them recognize obstacles better. The Roborock S7, on the other hand, only has LIDAR and ultrasonic sensors—no camera—but it creates a more detailed laser map, which I noticed makes the vacuum cleaner clean closer to the walls. For recognizing furniture, they usually use cameras and 3D sensors. The Roborock's vacuum can even scan under my couch thanks to its 3D sensor, so it doesn’t get stuck there. Xiaomi, however, uses the camera to create speckled patterns over time and compares them with the vacuum’s movements to identify obstacles. Also, each brand has its own functional names—like Roborock’s "Reactive Tech," where the vacuum instantly adjusts its path near edges. Xiaomi’s system is more algorithm-based; it maps the area slowly on the first pass but cleans super efficiently on the second round.

23 Ağu 13:14

Laren's robot vacuums commonly use a ring-shaped LIDAR sensor for mapping, meaning 360-degree laser scanning. This system typically maps room edges and obstacles with high accuracy, allowing the vacuum to navigate without getting lost. For example, Roborock and Eufy models combine this method with camera sensors, especially to assist in low-light conditions. To recognize household furniture, both depth sensors (Time of Flight, ToF) and AI-powered vision systems are used. In Sennheiser’s latest model, the camera continuously captures images to identify objects like tables and chairs, guiding the vacuum around them. Xiaomi’s systems, on the other hand, use an AI model that works in sync with LIDAR data to create a 3D map of the scanned area—this way, furniture isn’t just stored as "obstacles" but as recognizable objects. The key differences between brands emerge here: high-end models that deliver efficient cleaning often combine high-precision LIDAR and ToF sensors, while budget models (e.g., the base Roomba) may rely only on laser scanning and basic camera algorithms. If you use the vacuum across multiple floors, this can lead to performance drops in mapping accuracy.