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 multi- level spatial representation architecture, our approach inte- grates methods for mapping, self-localization and spatial rea- soning for navigation with visual attention, feature detection, and hierarchical neural classifiers. By combining probabilis- tic representations with qualitative spatial representations, the robot can robustly localize and navigate to previously de- tected objects, and also associate symbolic knowledge with task-relevant objects, which is essential for task planning and interaction with humans.