 Dear Fellow Scholars, this is 2 Minute Papers with Károly Zsolnai-Fehér. The goal of this work is to have a robot that automatically creates a 3D model of an indoor space, including path planning and controlling attention. Now this immediately sounds like quite a challenging task. This robot uses an RGB-D camera, so beyond the colors it also gets some depth information that describes how far the viewed objects are from the observer. From this information, it tries to create a 3D digital model of these interiors. It not only does that, but it also constantly replants its trajectory based on newly found areas. These paths have to be smooth and walkable without bumping into objects and of course adapt to the topology of the building. You'll see in a moment why even this mundane sounding smooth part is really challenging to accomplish. Spoiler alert, it's about singularities. With this proposed technique, the robot takes a lap in the building and builds a rough representation of it, kind of like a mini-map in your favorite computer games. Previous techniques worked with potential and gradient fields to guide the navigation. The issue with these is that the green dots that you see here represent singularities. These are the generate points that introduce ambiguity to the path planning process and reduce the efficiency of the navigation. The red dots are sinks, which are even worse because they can trap the robot. This new proposed tensor field representation contains a lot fewer singularities and its favorable mathematical properties make it sink free. This leads to much better path planning, which is crucial for maintaining high reconstruction quality. If you have a look at the paper, you'll see several occurrences of the word advection. This is particularly cool because these robot paths are planned in these gradient or tensor fields represent the vertices and flow directions similarly to how fluid flows are computed in simulations, many of which you have seen in this series. Beautiful. Love it. However, as advection can't guarantee that we'll have a full coverage of the building, this proposed technique borrows classic structures from graph theory. Graph theory is used to model the connections between railway stations or to represent people and their relationships in a social network. And here, a method to construct a minimum spanning tree was borrowed to help deciding which direction to take at intersections for optimal coverage with minimal effort. Robots, fluids, graph theory, some of my favorite topics of all time, so you probably know how happy I was to see all these theories come together to create something really practical. This is an amazing paper. Make sure to have a look at it. It is available in the video description. The source code of this project is also available. If you have enjoyed this episode, make sure to subscribe to the series and click the bell icon to never miss an episode. Thanks for watching and for your generous support and I'll see you next time.