Sure thing. Start with the drones on the ground. Each drone packs a small compute unit to do quick preprocessing—filter out sensor noise, align the point clouds with a tiny onboard GPS. Then it streams the raw LiDAR or stereo data over 5G to a nearby edge gateway. The gateway aggregates a handful of streams, runs a fast mesh‑generation routine, and pushes the resulting partial meshes to a cloud service via a websocket. In the cloud you stitch all the partial meshes into a global model, run a robust SLAM algorithm to keep the coordinates tight, and expose a WebSocket API that pushes incremental updates to a database. The database is a spatial‑indexed NoSQL store (think DynamoDB with Geo‑Index or MongoDB with 3D indexing). On top of that you run a real‑time analytics layer that tags features, detects obstacles, and calculates the best paths. Finally, you feed the updated mesh and analytics into your AR engine over WebGL. Clients load a lightweight 3D scene, subscribe to the update stream, and render the latest map. That’s the pipeline—edge preprocessing, streaming to cloud, stitching, database, and AR consumption.

Nice tweak—give the drones a tiny SLAM so they can cut out the bulk of raw data and just send delta meshes. On the edge, a gossip layer lets each node pull the latest chunks from its neighbors, so the whole swarm stays in sync without a bottleneck. The edge aggregates those deltas, does a quick consistency check, and pushes only the new vertices to the cloud for the heavy stitching and analytics. The cloud then builds a global point cloud, runs the big SLAM for the final map, and streams incremental updates back to the AR front end. Keep the messaging lightweight, use protobuf over WebSocket, and you’ll have low latency for the explorers while the back end stays out of the way.

Add a tiny CNN on each drone that runs on the on‑board GPU or a tiny TPU. Feed it the latest stitched mesh slice and a history of traversal vectors; it outputs a heat‑map of likely high‑grade openings. Each edge node runs a simple decision layer that aggregates those heat‑maps from its neighbors—so if one drone sees a promising cavity, the gossip spread flags it to the whole swarm. Then the flight controller uses a lightweight A* over the predicted hotspots to pick the next waypoint. All the training data gets fed back to the cloud where a larger model refines the predictions, and the drones keep getting better without pulling heavy weights down the line. That’s your “auto‑seek” AI in the loop.