| LEVEL - DIM. < 3 | TOLERANCE TYPE: A ±0.03, B ±0.03, C ±0.1, D ±0.15 |
| LEVEL - DIM. >3~50 | TOLERANCE TYPE: A ±0.05, B ±0.05, C ±0.1, D ±0.15 |
| LEVEL - DIM. >50~100 | TOLERANCE TYPE: A ±0.05, B ±0.08, C ±0.15, D ±0.2 |
| LEVEL - DIM. >100 | TOLERANCE TYPE: A ±0.08%, B ±0.1%, C ±0.2, D ±0.3% |
| LEVEL - ANGLE | TOLERANCE TYPE: A ±0.5°, B ±0.5°, C ±0.8°, D ±3° |
| View angle method | - |
| Size | A4 |
| Unit | mm |
| Scale | 1:1 |
| Device type | CZM2D4550 - V1 |
| Machine | - |
| Electric | - |
| Design | QXW |
| Design Date | 09/14 - '24 |
| Check | LJJ |
| Check Date | 09/14 - '24 |
| Approval | XZL |
| Approval Date | 09/14 - '24 |
| Sheet No. | 1 / 1 |
Premium Services We offer customized services and support for relevant development driver packages.
For any other requirements, please email:union@unimage.com.cn
For any other requirements, please email:union@unimage.com.cn
1.Night -time Obstacle Avoidance and Full -timeOperation
Navigation in Lightless Environments
Night -time Obstacle Detection
2.Depth Sensing and Intelligent Obstacle Avoidance
Stereo Depth Estimation
Recognition of Low - lying Obstacles
3.Detection of Transparent Objects and SpecialObstacles
Recognition of Glass Doors/Transparent Furniture
Detection of Highly Reflective Surfaces
4.Boundary Recognition and Fall -prevention Protection
Fall -prevention on Stairs
Virtual Wall Positioning Assistance
5.Recognition of Specific Objects and OperationOptimization
Recognition of Pets/Pet Waste (Combined with AlAlgorithms)
Abnormal Environment Monitoring
6.Sensor Fusion and Positioning Enhancement
Fusion with LiDAR and Ultrasonic Sensors
Adaptive Switchingin High -light Environments
Navigation in Lightless Environments
Night -time Obstacle Detection
2.Depth Sensing and Intelligent Obstacle Avoidance
Stereo Depth Estimation
Recognition of Low - lying Obstacles
3.Detection of Transparent Objects and SpecialObstacles
Recognition of Glass Doors/Transparent Furniture
Detection of Highly Reflective Surfaces
4.Boundary Recognition and Fall -prevention Protection
Fall -prevention on Stairs
Virtual Wall Positioning Assistance
5.Recognition of Specific Objects and OperationOptimization
Recognition of Pets/Pet Waste (Combined with AlAlgorithms)
Abnormal Environment Monitoring
6.Sensor Fusion and Positioning Enhancement
Fusion with LiDAR and Ultrasonic Sensors
Adaptive Switchingin High -light Environments
