Concurrent Object Identification and Localization for a Mobile Robot

Identification and localization of task-relevant objects is an essential problem for advanced service robots. We integrate state-of-the-art techniques both for object identification and object localization to solve this problem. Based on a multilevel spatial representation architecture, our approach integrates methods for mapping, self-localization and spatial reasoning for navigation with visual attention, feature detection, and hierarchical neural classifiers. By combining probabilistic representations with qualitative spatial representations, the robot can robustly localize and navigate to previously detected objects, and also associate symbolic knowledge with task-relevant objects, which is essential for task planning and interaction with humans.