Publication
ExPrIS: Knowledge-Level Expectations as Priors for Object Interpretation from Sensor Data
Marian Renz; Martin Günther; Felix Igelbrink; Oscar Lima; Martin Atzmueller
In: Lars Kunze (Hrsg.). KI - Künstliche Intelligenz, German Journal on Artificial Intelligence - Organ des Fachbereiches "Künstliche Intelligenz" der Gesellschaft für Informatik e.V. (KI), Springer Nature, 2026.
Abstract
While deep learning has significantly advanced robotic object recognition, purely data-
driven approaches often lack semantic consistency and fail to leverage valuable, pre-
existing knowledge about the environment. This report presents the ExPrIS project,
which addresses this challenge by investigating how knowledge-level expectations can
serve as priors to improve object interpretation from sensor data. Our approach centers
on the incremental construction of a 3D Semantic Scene Graph (3DSSG). We integrate
expectations from two sources: contextual priors from past observations and semantic
knowledge from external graphs like ConceptNet. These are embedded into a
heterogeneous Graph Neural Network (GNN) to create an expectation-biased
inference process. This method moves beyond static, frame-by-frame analysis to
enhance the robustness and consistency of scene understanding over time. The report
details this architecture, its evaluation, and its integration on a mobile robotic platform.