Unsupervised discovery of object classes with a mobile robot

Object detection and recognition are fundamental capabilities for a mobile robot. Objects are a powerful representation for a variety of tasks including mobile manipulation and inventory tracking. As a result, object-based world representations have seen a great deal of research interest in the last several years. However, these systems usually assume that object recognition is well-solved: they require that accurate recognition be available for every object they might encounter. Despite steady advances, object recognition remains a difficult, open problem. Existing object recognition algorithms rely on high-resolution three-dimensional object models or on extensive hand-labeled training data. The sheer variety of objects that occur in natural environments makes manually training a recognizer for every possible object infeasible. In this work, we present a robotic system for unsupervised object and class discovery, in which objects are first discovered, and then grouped into classes in an unsupervised fashion. At each step, we approach the problem as one of robotics, not disembodied computer vision. On a very large robotic dataset, we discover object classes with 98.7% precision while achieving 71.8% recall. The scale and quality of these results demonstrate the merit of our approach, and prove the practicality of long-term large-scale object discovery. To our knowledge, no other authors have investigated robotic object discovery at this scale, making direct quantitative comparison impossible. We make our implementation and ground-truth labelings available, and evaluate our technique on a very large dataset. As a result, this work is a baseline against which future work can be compared.