MagSlim LiDAR Navigation
What Makes It Stand Out?
What Makes It Stand Out?
MagSlim LiDAR Navigation adopts the mini-dToF LiDAR sensor and sinks it into the front of a robot vacuum for navigation and mapping, creating a new product form for LiDAR navigation robot vacuums. Like magic, it makes products extremely thin while retaining the precise mapping of traditional LiDAR navigation.
Seeing through Sensors
Gyroscope sensors detect the motion and rotation of the robots.
Efficient path planning
Introduced zigzag path planning to reduce omissions.
Lower accuracy
The measurement accuracy is lower, which doesn’t suit complex environments.
LiDAR scans the house with a spinning laser on top of the robot.
Precise mapping
LiDAR accurately measures distances and dimensions, generates precise house maps, and conducts planned cleaning.
Increased product height
However, subject to the sensor’s position, the LiDAR module increases the thickness of the robot.
The camera on the top maps the house by taking photos of the ceiling and other landmarks.
Visual mapping
It obtains more data by simulating human vision to realize the recognition and navigation of surroundings.
Limited operating condition and accuracy
Every coin has two sides, though, VSLAM’s accuracy is reduced and cannot work in the dark.
The newly designed Mini-dToF LiDAR is sunk into the interior of the robot to map and navigate.
Precise mapping + ultra-slim
It not only continues with high-precision mapping and navigation but also bypasses the height limitation of traditional LiDAR. This provides the robot access beneath lower cabinets and beds to clean harder-to-reach areas.
The Mini-dToF LiDAR sensor adopts a sophisticated design to make it more compact. Different from traditional LiDAR sensors originally placed on the top of robots, the Mini-dToF LiDAR can be sunk into the interior of the robot. This reduces the vacuum’s height by 2 cm and enables entrance into narrower spaces to sweep away dust and dirt that are hard to find and clean, greatly increasing the robot vacuum’s cleaning coverage.
Through continuous technical iteration, the measurement accuracy of MagSlim LiDAR Navigation far exceeds that of VSLAM and gyroscope navigation. It achieves millimeter-level measurement and has a scanning distance of up to 8 m, perceiving and mapping the 3D world faster and more accurately.
High Sampling Frequency
4500 Hz
Captures more data, enriches sampling depth.
Wide Range Scanning Distance
0.03 m ~ 8 m
Measures objects near and far precisely.
Anti Light Interference
60000 Lux
Works well in both bright and dark rooms.
High-Level Laser Safety
IEC-60825 Class 1
Eye-safe under all operating conditions.
The mapping of MagSlim LiDAR Navigation combined with our self-developed smart path planning algorithm enables the robot vacuum to clean every corner of your home, reducing repeated cleaning and omissions, greatly increasing cleaning coverage, and shortening the entire process time.
Cleaning Coverage
Over 90%
The Mini-dToF LiDAR adopts a purely solid-state design without a mechanical drive, which lowers its failure rate. Compared with traditional LiDAR, its lifespan is 5× longer, up to 10000 hours, reducing maintenance and replacement costs.
Mini-dToF LiDAR
Traditional LiDAR
Gyroscope Navigation | VSLAM Navigation | LiDAR Navigation | MagSlim LiDAR Navigation | |
---|---|---|---|---|
Sensor Lifespan | / | 10000 h | 2000 h | 10000 h |
Sensor Dimensions | Extra Small | Medium | Large | Small |
Sampling Frequency | / | / | 2300 Hz ~ 3000 Hz | 4500 Hz |
Scanning Range | / | / | 0.16 m ~ 8 m | 0.03 m ~ 8 m |
Anti-Ambient Light | / | / | 30000 Lux | 60000 Lux |
Works Well in the Dark | Yes | No | Yes | Yes |
Height of the Body | ≤8 cm | 8 cm ~ 10 cm | 9 cm ~ 10 cm | ≤8 cm |
Mapping Accuracy | ★★ | ★★★ | ★★★★ | ★★★★ |
Cleaning Coverage | ★★★ | ★★ | ★★★★ | ★★★★ |
Scheduled Cleaning | Yes | Yes | Yes | Yes |
Multi-Floor Maps | No | Yes | Yes | Yes |
No-Go Zones / Virtual Walls | No | Yes | Yes | Yes |
Selected Room Cleaning | No | Yes | Yes | Yes |
Specific Area Cleaning | No | Yes | Yes | Yes |
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