Much of the focus in the object detection literature has been on the problem of identifying the bounding box of a particular class of object in an image. Yet, in contexts such as robotics and augmented reality, it is often necessary to find a specific object instance-a unique toy or a custom industrial part for example-rather than a generic object class. Here, applications can require a rapid shift from one object instance to another, thus requiring fast turnaround which affords little-to-no training time. What is more, gathering a dataset and training a model for every new object instance to be detected can be an expensive and time-consuming process. In this context, we propose a generic 2D object instance detection approach that uses example viewpoints of the target object at test time to retrieve its 2D location in RGB images, without requiring any additional training (i.e. fine-tuning) step. To this end, we present an end-to-end architecture that extracts global and local information of the object from its viewpoints. The global information is used to tune early filters in the backbone while local viewpoints are correlated with the input image. Our method offers an improvement of almost 30 mAP over the previous template matching methods on the challenging Occluded Linemod [3] dataset (overall mAP of 50.7). Our experiments also show that our single generic model (not trained on any of the test objects) yields detection results that are on par with approaches that are trained specifically on the target objects.

Accurate pose estimation is often a requirement for robust robotic grasping and manipulation of objects placed in cluttered, tight environments, such as a shelf with multiple objects. When deep learning approaches are employed to perform this task, they typically require a large amount of training data. However, obtaining precise 6 degrees of freedom for ground-truth can be prohibitively expensive. This work therefore proposes an architecture and a training process to solve this issue. More precisely, we present a weak object detector that enables localizing objects and estimating their 6D poses in cluttered and occluded scenes. To minimize the human labor required for annotations, the proposed detector is trained with a combination of synthetic and a few weakly annotated real images (as little as 10 images per object), for which a human provides only a list of objects present in each image (no time-consuming annotations, such as bounding boxes, segmentation masks and object poses). To close the gap between real and synthetic images, we use multiple domain classifiers trained adversarially. During the inference phase, the resulting class-specific heatmaps of the weak detector are used to guide the search of 6D poses of objects. Our proposed approach is evaluated on several publicly available datasets for pose estimation. We also evaluated our model on classification and localization in unsupervised and semi-supervised settings. The results clearly indicate that this approach could provide an efficient way toward fully automating the training process of computer vision models used in robotics.

Pick-and-place is an important task in robotic manipulation. In industry, template-matching approaches are often used to provide the level of precision required to locate an object to be picked. However, if a robotic workstation is to handle numerous objects, brute-force template-matching becomes expensive, and is subject to notoriously hard-to-tune thresholds. In this paper, we explore the use of Deep Learning methods to speed up traditional methods such as template matching. In particular, we employed a Single Shot Detection (SSD) and a Residual Network (ResNet) for object detection and classification. Classification scores allows the re-ranking of objects so that template matching is performed in order of likelihood. Tests on a dataset containing 10 industrial objects demonstrated the validity of our approach, by getting an average ranking of 1.37 for the object of interest. Moreover, we tested our approach on the standard Pose dataset which contains 15 objects and got an average ranking of 1.99. Because SSD and ResNet operates essentially in constant time in a Graphics Processor Unit, our approach is able to reach near-constant time execution. We also compared the F1scores of LINE-2D, a state-of-the-art template matching method, using different strategies (including our own) and the results show that our method is competitive to a brute-force template matching approach. Coupled with near-constant time execution, it therefore opens up the possibility for performing template matching for databases containing hundreds of objects.

This paper proposes a complete system for robotic sensor placement in initially unknown arbitrary three-dimensional environments. The system uses a novel approach for computing the quality of acquisition of a mobile sensor group in such environments. The quality of acquisition is based on a geometric model of a camera which allows accurate sensor models and simple occlusion computation. The proposed system combines this new metric with a global derivative-free optimization algorithm to find simultaneously the number of sensors and their configuration to sense accordingly the environment. The presented framework compares favourably with current techniques working in two-dimensional environments. Furthermore, simulation and experimental results demonstrate the ability of the system to cope with full three-dimensional environments, a domain still unexplored by previous methods.