ExPrIS: Knowledge-Level Expectations as Priors for Object Interpretation from Sensor Data
This work addresses the challenge of semantic inconsistency in robotic perception, offering an incremental improvement for mobile robotics applications.
The paper tackles the problem of robotic object recognition lacking semantic consistency by integrating knowledge-level expectations into a 3D Semantic Scene Graph using a Graph Neural Network, resulting in enhanced robustness and consistency in scene understanding over time.
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 to improve object interpretation from sensor data. Our approach is based 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 outlines its planned integration on a mobile robotic platform.