Redefining robot precision and speed with AI vision

XEMA Structured Light 3D Camera

Fast capture, detailed imaging, proven stability, and strong environmental adaptability

Point Clouds

2D ImagePoint cloud
Point-cloud cases

  • 0.5s fast imaging
  • Full part data in a single capture — no frame stitching.

  • Up to 1μm Z-axis repeatability.
  • Consistent measurement and positioning, part after part

  • Robot guidance for loading and unloading, positioning, and assembly.
  • SDK and standard industrial interfaces for fast deployment.

Solutions

Loading and Unloading

AI + 3D vision guided workpiece handling

AI + 3D vision guide robots to pick randomly stacked or closely packed parts from bins for transfer or further processing. The solution supports workpiece handling in automotive, logistics,construction machinery, and steel manufacturing.

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Loading and Unloading

AI + 3D vision guided workpiece handling

AI + 3D vision guide robots to pick randomly stacked or closely packed parts from bins for transfer or further processing. The solution supports workpiece handling in automotive, logistics,construction machinery, and steel manufacturing.

Learn more
Positioning and Assembly

AI + 3D vision guided positioning and assembly

AI + 3D vision identify workpiece position and orientation in real time to guide precise picking, positioning, and assembly. It handles different part sizes and flexible production lines, improving assembly consistency and cutting changeover time.

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Specifications

Product model
XEMA-P
XEMA-P
XEMA-DCW
XEMA-DCW
XEMA-SCW
XEMA-SCW
XEMA-LCW
XEMA-LCW
XEMA-S
XEMA-S
XEMA-LC
XEMA-LC
Field of view (H/V)
Dimensions (mm)
Weight (kg)
Baseline (mm)
Resolution (px)
Interface
Computing unit
Rated voltage
Operating temperature
Certifications
Protection rating
Near field of view (mm)
Far field of view (mm)
Recommended distance (mm)
Z-axis accuracy
Z-axis repeatability σ (μm)
Pixel pitch
Typical acquisition time (s)
Output data
Operating systems
SDK interfaces
35°/26°
126×146×61
1.10
70
2448×2048
Gigabit Ethernet
NVIDIA Jetson nano
DC 24V 3.2A
-20 - 40
CE、FCC、RoHS、KC
IP6X
56×39
88×61
100-150
6μm@0.1m
1@0.10-0.15m
22μm@0.10m 33μm@0.15m
1.0-2.0
Point cloud, depth map, grayscale image
Windows, Linux (Ubuntu 20.04)
C/C++/C#/Python
58°/33°
119×127×58
0.80
80
1920×1200
Gigabit Ethernet
NVIDIA Jetson nano
DC 24V 3.2A
-20 - 40
CE、FCC、RoHS、KC
IP65
287×171
523×286
300-500
0.03mm@0.4m
21@0.3-0.5m
0.16mm@0.3m 0.27mm@0.5m
0.5-1.5
Point cloud, depth map, grayscale image
Windows, Linux (Ubuntu 20.04)
C/C++/C#/Python
58°/35°
207×128×46
1.30
145
1920×1200
Gigabit Ethernet
NVIDIA Jetson nano
DC 24V 3.2A
-20 - 40
CE、FCC、RoHS、KC
IP65
488×296
1021×593
500-1000
0.05mm@0.8m
41@0.5-1.0m
0.27mm@0.5m 0.55mm@1.0m
0.5-1.5
Point cloud, depth map, grayscale image
Windows, Linux (Ubuntu 20.04)
C/C++/C#/Python
50°/33°
433×133×45
1.80
350
1920×1200
Gigabit Ethernet
NVIDIA Jetson nano
DC 24V 3.2A
-20 - 40
CE、FCC、RoHS、KC
IP65
965×569
2462×1453
1000-2500
0.50mm@2.5m
80@1.0-2.5m
0.55mm@1.0m 1.37mm@2.5m
0.5-1.5
Point cloud, depth map, grayscale image
Windows, Linux (Ubuntu 20.04)
C/C++/C#/Python
43°/23°
207×127×50
1.2
145
1920×1200
Gigabit Ethernet
NVIDIA Jetson Nano
DC 24V 3.2A
-20 - 40
CE、FCC、RoHS、KC
IP65
390×220
770×430
500-1000
0.07mm@0.8m
41@0.5m-1m
0.2mm@0.5m 0.4mm@1.0m
0.5-1.5s
Point cloud, depth map, grayscale image
Windows, Linux (Ubuntu 20.04)
C/C++/C#/Python
38°/23°
433×133×51
1.9
350
1920×1200
Gigabit Ethernet
NVIDIA Jetson Nano
DC 24V 3.2A
-20 - 40
CE、FCC、RoHS、KC
IP65
685×405
1702×1039
1000-2500
0.5mm@2.5m
80μm@1m-2.5m
0.36mm@1m 0.9mm@2.5m
0.5-1.5s
Point cloud, depth map, grayscale image
Windows, Linux (Ubuntu 20.04)
C/C++/C#/Python