6D pose estimation aims at determining the object pose that best explains the camera observation. The unique solution for non-ambiguous objects can turn into a multimodal pose distribution for symmetrical objects or when occlusions of symmetry-breaking elements happen, depending on the viewpoint. Currently, 6D pose estimation methods are benchmarked on datasets that consider, for their ground truth annotations, visual ambiguities as only related to global object symmetries, whereas they should be defined per-image to account for the camera viewpoint. We thus first propose an automatic method to re-annotate those datasets with a 6D pose distribution specific to each image, taking into account the object surface visibility in the image to correctly determine the visual ambiguities. Second, given this improved ground truth, we re-evaluate the state-of-the-art single pose methods and show that this greatly modifies the ranking of these methods. Third, as some recent works focus on estimating the complete set of solutions, we derive a precision/recall formulation to evaluate them against our image-wise distribution ground truth, making it the first benchmark for pose distribution methods on real images.

We introduce the CEA Industrial Multi-Scale Object Localization Dataset (CEA-IMSOLD), a new BOP format dataset for 6-DoF object localization, crucial for robotics. This dataset aims to evaluate the current localization methods with respect to a new difficulty: large variations in observation distance and, consequently, large variations in image appearance. Compared to the other publicly available datasets, our dataset provides both images with objects small and completely visible in the image, and images where objects are observed close enough so they appear larger than the field of view of the camera. We also propose to consider the observation distance in the evaluation process and introduce new metrics to do so. Finally, our dataset contains a large variety of industrial objects, from small and simple objects such as bolts to sizable and complex ones such as large car parts. We provide baseline results and the dataset is made publicly available to support the community at https://cea-list.github.io/CEA-IMSOLD/.

To overcome the limitations of the so-called Industry 4.0 focusing on mass production and full automation, a novel paradigm was recently introduced, namely Industry 5.0, which aims at an increased collaboration between humans and machines, and particularly robots, instead of replacing the former with the latter. This challenge requires novel interactive intelligent robots able to perform complex tasks easily and efficiently and to collaborate on the fly with humans whenever required, be it for training or working. In this work, the Robot Companion, a novel demonstrator of this paradigm, is introduced. It combines robotics, Artificial Intelligence, software engineering and embedded systems technologies, and targets industrial assembly tasks. First tests show that this robot can efficiently assemble a representative gear system autonomously or in collaboration with human operators.

We present a comprehensive augmented reality solution to efficiently perform different verification pocedures during industrial assembly-line manufacturing using CAD data from product lifecycle management (PLM) systems. The demonstration focuses on a variety of industrial use-cases that have to go through different control processes, assisted by augmented reality tools. Thus, the experience has to be precise, robust to natural movements and easy to realise for an assembly-line technician.