German researchers are teaching robots to see

SIMTOS (Seoul International Machine Tool Show)

Caution! Seeing does not automatically mean understanding!

Before a program can begin to understand its environment, it must first be able to perceive it. Countless sensors provide their data for this purpose, and these are then combined into an overall impression, explains the DFKI. A robot then uses this to orient itself in the room. The problem is, as with the human eye, there are overlaps in the visual information. To understand this and generate a consistent picture from the multitude of data, the DFKI has developed the "SG-PGM" (Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and its Downstream Tasks). The alignment between so-called three-dimensional scene visualizations (3D scene graphs) forms the basis for many applications. For example, it supports "point cloud registration" and helps robots to navigate correctly. To ensure this even in dynamic environments with possible sources of interference, "SG-PGM" links the visualizations with a neural network. The program then utilizes the geometric elements that were learned through the point cloud registration and associates the grouped geometric point data with the semantic features at the node level.

A program recognizes objects based on their meaning.

Basically, a certain grouping of points is assigned a semantics (for example, the meaning: "blue chair in front of the monitor"). The same grouping can then be recognized again in another graph and thus the scene can be extended only by the non-recurring elements. "SG-PGM" is therefore able to identify any overlaps in a scene with unprecedented accuracy and to determine the most precise overall image possible with the many sensors. This means that robots can then find their way better in three-dimensional space and can precisely locate objects. This progress was honored by the organizers of the CVPR (Conference on Computer Vision and Pattern Recognition) in Seattle with a placement. With a total of six different papers, the DFKI team now plans to present technologies there, among other things, that can identify objects in three-dimensional space based on variable linguistic descriptions and can comprehensively capture and map the environment with sensors.

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Date: 08.12.2025

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Robot perception