  Taking over arbitrary tasks like humans do with a mobile service robot in
open-world settings requires a holistic scene perception for decision-making
and high-level control. This paper presents a human-inspired scene perception
model to minimize the gap between human and robotic capabilities. The approach
takes over fundamental neuroscience concepts, such as a triplet perception
split into recognition, knowledge representation, and knowledge interpretation.
A recognition system splits the background and foreground to integrate
exchangeable image-based object detectors and SLAM, a multi-layer knowledge
base represents scene information in a hierarchical structure and offers
interfaces for high-level control, and knowledge interpretation methods deploy
spatio-temporal scene analysis and perceptual learning for self-adjustment. A
single-setting ablation study is used to evaluate the impact of each component
on the overall performance for a fetch-and-carry scenario in two simulated and
one real-world environment.
