Build a Computer-Vision Navigation System for Robot Vacuums

1. 1

   Build the perception and SLAM core

   The product is mapping (SLAM), obstacle and object recognition, and path planning that works reliably in messy real homes with pets, cables, and clutter. This is the hard AI and robotics engineering the hardware brands do not want to build themselves, so it must genuinely outperform what they could do in-house.

2. 2

   Run on the compute they actually use

   Robot vacuums use cost- and power-constrained embedded compute, so your stack must run efficiently on those chips, not just in the cloud or on a workstation. Optimizing perception to run on real, cheap edge hardware is a large part of the value and the difficulty.

3. 3

   Prove it on real robots

   Brands will not license perception they have not seen navigate a real home. Validate on actual robot hardware across varied home conditions, and be ready to demonstrate obstacle avoidance and mapping quality, because a demo on real hardware is what closes a licensing deal.

4. 4

   Respect the privacy of what the cameras see

   Vision navigation processes images of people's homes, so design for on-device processing and privacy from the start; brands increasingly need to promise that camera data stays private (see the AI robot brand card here). A privacy-conscious stack is easier for brands to adopt and defend.

5. 5

   License to brands and adjacent robotics

   Sell as licensed software or an SDK to robot-vacuum makers, and to adjacent consumer robotics (mowers, service robots) that share the perception problem. Serving multiple brands and categories is how a software company amortizes deep R and D across many customers.

6. 6

   Support integration and keep improving

   Licensing includes helping brands integrate the stack and improving it over time as edge cases surface. Ongoing model improvement, integration support, and per-unit or per-device licensing turn a one-time deal into a scaling, high-margin software relationship.

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