Fast self-supervised on-line training for object recognition specifically for robotic applications

Today most recognition pipelines are trained at an off-line stage, providing systems with pre-segmented images and predefined objects, or at an on-line stage, which requires a human supervisor to tediously control the learning. Self-Supervised on-line training of recognition pipelines without human intervention is a highly desirable goal, as it allows systems to learn unknown, environment specific objects on-the-fly. We propose a fast and automatic system, which can extract and learn unknown objects with minimal human intervention by employing a two-level pipeline combining the advantages of RGB-D sensors for object extraction and high-resolution cameras for object recognition. Furthermore, we significantly improve recognition results with local features by implementing a novel keypoint orientation scheme, which leads to highly invariant but discriminative object signatures. Using only one image per object for training, our system is able to achieve a recognition rate of 79% for 18 objects, benchmarked on 42 scenes with random poses, scales and occlusion, while only taking 7 seconds for the training. Additionally, we evaluate our orientation scheme on the state-of-the-art 56-object SDU-dataset boosting accuracy for one training view per object by +37% to 78% and peaking at a performance of 98% for 11 training views.