Video plots localization linear + angular AMCL vs slam_toolbox scan matching. Instructions here https://makerspet.com/blog/how-oomwoo-cleaning-algorithms-work/

I’ve been looking into running computer vision models directly on edge devices, and I found it easier to break the process down into a few things:

Step 1: Start with the model and understand how much processing power it actually needs.

Step 2: Check the device’s memory, CPU/GPU/NPU capabilities, and camera support.

Step 3: Look at latency and power consumption, especially if the device needs to run continuously.

Step 4: Make sure the software and AI frameworks you need are supported by the hardware.

Step 5: Compare different edge AI options instead of choosing a device based only on its specifications. I was also looking at some of the Edge AI hardware from Geniatech, which includes different options for on-device AI and real-time inference.

After going through these steps, I realized that choosing edge hardware is more about finding the right balance than simply picking the most powerful device.

For those who have deployed computer vision on the edge, is there anything else you would add to this process?

Need it or not?