Smart mapping for robots exploring huge areas! 🗺️ This is called Rolling-Bonxai. When a drone or ground robot maps a massive area, say a whole city or massive warehouse, storing the entire map in memory gets expensive fast. You run out of RAM. This solution is simple, only keep... the nearby map in memory. As the robot moves, old chunks save to disk and new chunks load. Like how video games only render what's close to the player. The system divides the map into chunks (imagine a 3D grid of boxes). As the robot explores, it keeps chunks around it loaded in memory. Chunks it's moving away from get saved to disk. When it comes back, they load again. Check the GH repo here 👉🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →show more

Rolling-Bonxai trades full-map RAM for disk I/O latency on reloads. That mirrors the sim-to-real transfer bet I wrote about last month. The real test is whether chunk swaps stay fast enough for continuous locomotion.

Where do you keep on finding banger upon banger codes and papers in robotics? Do you taste like the sauce because you are dipped in the sauce? Or is it because you know where to find the best sauce?

Chunked voxel grid. Watch for… - boundaries -> thrashing -> planning stalls - integration errors -> drift -> can’t find right chunks - map fragmentation -> loop closure -> ghosting - alt/range est. errors -> scale drift -> matching failures It’s simple until it meets the real world. 🙂

Sliding-window maps are the pragmatic answer for warehouse-scale AMRs too - you rarely need the full global map in RAM, just local occupancy plus anchors for loop closure. Curious how Rolling-Bonxai handles loop closure corrections once old chunks are already on disk.

@facontidavide check this out

That’s what they’ve always done with computer games

Your data is too dense. If the space is rectilinear just use a line and width and height.

Quite interesting how we developed solutions to these kinds of problems in video games.