Robot vacuum cleaners are pretty good at navigating rooms, but they still get stuck in tight spaces. New work takes a step toward giving them the smarts they’ll need to escape the bathroom.

What’s new: Led by Ameya Phalak, a team at Magic Leap created Scan2Plan, a model that segments 3D scans of empty indoor spaces into floor plans.

Key insight: Given a single scan covering an entire building, Scan2Plan learns to recognize scanned 3D points belonging to the same wall and those belonging to the same room. Once it knows the walls and rooms they form, generating a floor plan is easy.

How it works: 3D scanners project light and measure how long it takes to bounce back, producing a point cloud that represents the scene. In an empty room, these points are likely to belong to walls.

  • The team started by creating a synthetic training dataset. They generated random 2D shapes divided into sub-shapes, all made of straight lines. Then they extended the shapes and sub-shapes into a third dimension and placed 3D points on the sub-shape boundaries to represent walls.
  • The team adapted PointNet++, a neural net designed to process sets of points. For each point, the model predicted the 3D coordinates of the center of the wall it belonged to, the center of the room it belonged in, and the center of the adjoining room.
  • The researchers used DBSCAN to cluster the predicted coordinates. Clustering allows for imprecision in the point locations, so the center of a room doesn’t appear to belong to different rooms in the floor plan.
  • The company’s DeepPerimeter algorithm projects clusters that share rooms and walls onto a 2D plane to create a floor plan. Roughly speaking, DeepPerimeter draws lines between points in a wall cluster, merges those that overlap, and connects different walls.

Results: The team tested Scan2Plan on the Beijing Real Estate dataset. The network was over 100 times faster than the previous state of the art, Floor SP, while achieving better F1 scores for corners (0.915 versus 0.877) and walls (0.860 versus 0.788).

Yes, but: Much of Beijing Real Estate has been preprocessed to remove scanner noise. When noise was included, Floor SP achieved a better F1 score for corners, though similar results for walls and rooms.

Why it matters: Floor plans can help robots in tasks that require mapping their surroundings, such as localization. Although their performance is similar, Scan2Plan is much faster than Floor SP, producing floor plans in 4 seconds rather than 8 minutes.

We’re thinking: Standard supervised learning algorithms don’t have great ways to predict an arbitrary number of output classes, such as the number of rooms in a floor plan. Rather than try to predict a room’s identity directly (which is hard, because the world holds arbitrarily many rooms), this work predicted the location of the next room’s center. We continue to be impressed by the creativity of researchers working to fit supervised learning into larger systems.


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