Context Aware Robot Navigation using Interactively Built Semantic Maps
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We discuss the process of building semantic maps, how to interactively label entities in it, and use them to enable new navigation behaviors for specific scenarios. We utilize planar surfaces such as walls and tables, and static objects such as door signs as features to our semantic mapping approach. Users can interactively annotate these features by having the robot follow him/her, entering the label through a mobile app and performing a pointing gesture toward the landmark of interest. These landmarks can later be used to generate context-aware motions. Our pointing gesture approach can reliably estimate the target object using human joint positions and detect ambiguous gestures with probabilistic modeling. Our person following method attempts to maximize future utility by searching future actions, assuming constant velocity model for the human. We describe a simple method to extract metric goals from a semantic map landmark and present a human aware path planner that considers the personal spaces of people to generate socially-aware paths. Finally, we demonstrate context-awareness for person following in two scenarios: interactive labeling and door passing. We believe as the sensing technology improves and maps with richer semantic information becomes commonplace, it would create new opportunities for intelligent navigation algorithms.
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