Product Details
The Luxonis OAK-D-IoT-40 is an embedded/IoT version of the OAK-D imaging platform. It can work standalone or via USB with a host, just like normal USB - only DepthAI variants. It comes with a built - in ESP32 that offers WiFi, Bluetooth® (including BLE), and other interfaces. The OAK - SOM - IoT system on module powers this model. Its modular design lets you use open - source designs to embed OAK - D's power into your products or customize it as you need. Similar to the OAK - D, it has the BNO085 IMU supported by the DepthAI API. This embedded version has a 128MB NOR flash and boot - switch capabilities for USB or NOR - flash boot and SPI communication with the ESP32. It includes an OAK - SOM - IoT, heatsink, power adapter, and USB 3 cable. Key features are 1x 12MP 4K/60Hz color camera, 2x 720P/120Hz global - shutter mono cameras in a stereo pair, a MA2485 or MA2085 vision processor (512MB or 2GB), a 100 - pin modular connector with various interfaces, optional on - board storage, software compatible with OpenVINO, Python and C++ APIs for multiple functions, single 5V or 3.3V power input, and dimensions of 40 x 30 x 6mm. You can refer to DepthAI Documentation, DepthAI GitHub, and DepthAI Hardware GitHub for more details.
Using the Luxonis OAK - D - IoT - 40 is easy. You can either use it on its own or connect it to a host via USB. When connecting, make sure to use the provided USB 3 cable. If you want to use the WiFi or Bluetooth features, the built - in ESP32 will handle that. To power it, use a single 5V or 3.3V power input with the provided power adapter. For software setup, the Python and C++ APIs are there to help you configure neural inference, stereo depth, and other functions. It's compatible with OpenVINO, so you can leverage that for your projects. When it comes to maintenance, keep the heatsink clean to prevent overheating. Also, be careful when handling the 100 - pin modular connector to avoid damage. If you run into any issues, refer to the DepthAI Documentation, DepthAI GitHub, or DepthAI Hardware GitHub for solutions.
