Recognizing Objects In-the-wild: Where Do We Stand?

The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments. Despite decades of effort from the robotic and vision research communities, robots are still missing good visual perceptual systems, preventing the use of autonomous agents for real-world applications. The progress is slowed down by the lack of a testbed able to accurately represent the world perceived by the robot in-the-wild. In order to fill this gap, we introduce a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot. The dataset embeds the challenges faced by a robot in a real-life application and provides a useful tool for validating object recognition algorithms. Besides describing the characteristics of the dataset, the paper evaluates the performance of a collection of well-established deep convolutional networks on the new dataset and analyzes the transferability of deep representations from Web images to robotic data. Despite the promising results obtained with such representations, the experiments demonstrate that object classification with real-life robotic data is far from being solved. Finally, we provide a comparative study to analyze and highlight the open challenges in robot vision, explaining the discrepancies in the performance

Abstraction, ontology and task-guidance for visual perception in robots

For solving recognition tasks in order to navigate in unknown environments and to manipulate objects, humans seem to use at least the following crucial capabilities: abstraction (for storing higher-level concepts of things), common sense knowledge and prediction. Whereas the first and second provide the basis for situated recognition, the second and third serve for pruning the search space as it helps anticipating what (in an abstract sense) they will see next and where. The main goal of our current research is, how we could use such a kind of "common sense world knowledge" for guiding visual perception and understanding scenes. Therefore, we are combining an owl-ontology with the output of vision tools. The additional use of abstraction techniques tries to establish the possibility of detecting higher level concepts, such as arches composed of a variable number of parts. The goal is to finally find concepts such as doors and tables in arbitrary scenes in order to arrive at a generic recognition tool for home robots. The ontology should additionally provide task-specific information about the things to detect

Robotic Grasping of Unknown Objects

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