Xiaomiβs robot vacuum cleaners are no longer just βwheel vacuum cleanersβ; modern models are equipped with laser rangefinders (LDS), collision sensors, gyroscopes and even cameras, the latter raising the most questions: why would a robot need a video camera when it already does cleaning? Is it just a marketing ploy to justify the high price?
In fact, the camera in Xiaomiβs robot vacuum cleaners (such as the Mi Robot Vacuum-Mop 2 Pro, DreameBot Z10 Pro, or Xiaomi Robot Vacuum X10+) performs several critical tasks, from accurate mapping to fraud protection. In this article, we will analyze 5 key camera features, compare it to laser navigation (LDS) and explain which Xiaomi models use it, and also explain why some users turn off the camera after buying.
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1. Precise mapping: how the camera builds an apartment plan
The main task of the camera in robot vacuum cleaners is to create a detailed map of the room in real time. Unlike laser rangefinders (LDS), which scan space in one plane, the camera captures a three-dimensional picture taking into account the height of furniture, textures and even lighting.
How does it work in practice? A robot with a camera (e.g. Xiaomi Robot Vacuum) X10+) And it moves around the room, and it takes hundreds of pictures per second. SLAM (Simultaneous Localization and Mapping analyze these frames, compare them with previous ones and build them. 3D-And this is a model of the room:
- π More precisely defines the boundaries of rooms and the location of furniture (for example, does not confuse the leg of a chair with an obstacle).
- π Faster adaptation to changes (replaced furniture, new items).
- π― Builds a cleaning route taking into account the height of obstacles (does not try to climb under a low sofa).
By comparison, LDS robots (such as Xiaomi Mi Robot Vacuum-Mop 2 Lite) can see the world in 2D and can make mistakes with the height of objects, while the camera avoids typical problems such as when a vacuum cleaner gets stuck under the bed or encounters transparent obstacles (glass tables, mirrors).
Recognition and avoidance of obstacles: why the robot does not run into wires
One of the most annoying problems with robot vacuum cleaners is getting stuck in wires, laces, or small objects, and the camera is helping to solve this problem with technology. AI-Algorithms learn from thousands of images and learn to distinguish between them:
- 𧦠Socks, slippers, children's toys.
- π Wires, cables, charger cords.
- πΎ Animal trays, food bowls.
- πͺ Thresholds, fringed mats.
For example, in the DreameBot L10s Ultra, the camera works together with an infrared sensor to avoid collisions. If the robot "sees" a wire on the floor, it either circles it or stops and sends a notification to the Mi Home app with a photo of the obstacle.
But there's a caveat: the camera is only effective in good lighting, its accuracy drops in the dark, and the robot can miss small objects, so some users additionally use exclusion zones in the application or physical limiters (such as magnetic tapes).
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If your camera robot often βseesβ obstacles, check the lighting in the room. Bright light (but not direct sunlight!) improves recognition.
3. Visual Navigation vs LDS: Which is Best for Your Apartment
The debate over whether a camera or a laser (LDS) is better has been going on for years, and to get a sense of it, let's compare it in terms of key parameters:
| Parameter | Camera. | LDS (laser) |
|---|---|---|
| Map accuracy | βββββ (3D-model, recognizes textures) | ββββ (2D-map, may be wrong with height) |
| Working in the dark | ββ (need light) | βββββ (The laser works in any lighting environment) |
| Identification of obstacles | βββββ (AI It's different wires, toys, trays) | βββ (It only determines the shape and distance) |
| Cost | Expensive (requires a powerful processor to process video) | Cheaper (laser modules are cheaper than cameras + processors) |
| Privacy | β οΈ Potential risk (video can be hacked) | β No risks (laser does not capture images) |
The camera definitely wins in apartments with a lot of small obstacles (children, animals, wires), and LDS is better suited for spacious rooms with minimal furniture or low lighting.
Fun fact: some flagship models (such as the Xiaomi Robot Vacuum X10+) combine both types of navigation β a camera for object recognition and a laser for precise positioning, making them the smartest, but also the most expensive.
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If your apartment has a lot of small objects on the floor (toys, wires, shoes), the camera will be more useful than LDS. For minimalist interiors, laser navigation is quite effective.
4 Remote monitoring: how the camera protects the house
Few people know, but the camera in Xiaomi robot vacuum cleaners can be used not only for cleaning. Some models (for example, DreameBot D9 or Xiaomi Mi Robot Vacuum-Mop 2 Pro) allow you to:
- πΈ Take a photo on request from the Mi Home app (for example, to check if the door is closed).
- π¨ Receive notifications if the robot βsawβ movement in the absence of the owners.
- π View the history of routes with reference to photos (useful if the robot is stuck).
This makes a vacuum cleaner look like a smart video surveillance system, so if you're at work and a robot finds a stranger in your apartment, it can send you a photo and turn on the siren (in some models), which is not a replacement for a full-fledged surveillance camera, but as an added layer of security, it's a useful feature.
β οΈ Note: Remote viewing of photos from a robot vacuum cleaner may violate privacy laws in some countries, such as in the EU, shooting without the consent of all residents of an apartment is prohibited.
There are no legal restrictions in Russia or the CIS, but it's worth remembering the risks of hacking, and if a robot is connected to Wi-Fi, in theory, attackers can access its camera.
Disable remote access if you are not using it.|Update the robot firmware regularly|Do not connect the robot to public Wi-Fi networks|Use a complex password for your Mi Home account-->
5. AI-Cleaning: How a camera helps to wash floors and avoid carpets
In robotic vacuum cleaners with floor washing function (e.g. Xiaomi Mi Robot Vacuum-Mop 2 Pro or DreameBot W10), the camera plays a key role in automatically selecting cleaning mode.
- π§Ή Carpet. β Increases the power of suction, turns off the wash.
- π§½ Hard floor. β includes wet cleaning with optimal water consumption.
- π« No-go zones β He is walking around carpets that cannot be wet.
Without a camera, the robot can only focus on the humidity sensors or the user's zone settings in the app, which is less accurate: for example, it can start washing the carpet if you forgot to mark it on the map, and the camera automatically recognizes the texture and switches modes without you.
A case in point: if you spilled coffee in the kitchen, a robot with a camera will see a stain, increase the intensity of the washing in that area, and come back to it several times, and without a camera, it will simply walk along a given route, not paying attention to the contamination.
How does a robot determine the type of coating?
6 Why Some Turn Off Camera: Cons and Risks
Despite all the benefits, many users turn off the camera in the robot settings.
- Privacy: Not everyone likes a robot to βremoveβ their apartment. Yes, Xiaomi claims that video is not transmitted to servers, but technically it is possible (especially in China, where other data laws apply).
- Performance: Video processing requires a lot of resources, which can cause the robot to slow down or discharge faster.
- False positives: The camera sometimes confuses shadows with obstacles or "sees" dirt where there is none, which makes cleaning more time.
How to turn off the camera? Most models do this through the Mi Home app.
- Open the robot map.
- Go to Settings. β Navigation.
- Turn off the visual navigation option or AI-recognition.
Once turned off, the robot will only use LDS or gyroscopes, which can reduce cleaning accuracy but increase autonomy and privacy.
β οΈ Note: In some models (e.g. DreameBot) Z10 Pro, the camera is used not only for navigation, but also to correct laser errors, and if it is turned off, the robot can begin to "lose" in complex spaces (for example, with a lot of mirrors or glass partitions).