Knowing your limits - self-evaluation and prediction in object recognition

Abstract

Allowing a robot to acquire 3D object models autonomously not only requires robust feature detection and learning methods but also mechanisms for guiding learning and assessing learning progress. In this paper we present probabilistic measures for observed detection success, predicted detection success and the completeness of learned models, where learning is incremental and online. This allows the robot to decide when to add a new keyframe to its view-based object model, where to look next in order to complete the model, predicting the probability of successful object detection given the model trained so far as well as knowing when to stop learning.