Neural Radiance Fields (NeRFs) have shown impressive results for novel view synthesis when a sufficiently large amount of views are available. When dealing with few-shot settings, i.e. with a small set of input views, the training could overfit those views, leading to artifacts and geometric and chromatic inconsistencies in the resulting rendering. Regularization is a valid solution that helps NeRF generalization. On the other hand, each of the most recent NeRF regularization techniques aim to mitigate a specific rendering problem. Starting from this observation, in this paper we propose CombiNeRF, a framework that synergically combines several regularization techniques, some of them novel, in order to unify the benefits of each. In particular, we regularize single and neighboring rays distributions and we add a smoothness term to regularize near geometries. After these geometric approaches, we propose to exploit Lipschitz regularization to both NeRF density and color networks and to use encoding masks for input features regularization. We show that CombiNeRF outperforms the state-of-the-art methods with few-shot settings in several publicly available datasets. We also present an ablation study on the LLFF and NeRF-Synthetic datasets that support the choices made. We release with this paper the open-source implementation of our framework.

Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera; ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.

This paper presents an accurate and precise hand-eye calibration technique based on minimization of the reprojection error. Unlike traditional hand-eye calibration, the proposed method does not require an explicit estimate of the camera pose for each input image because it does not rely on mathematical description and problem formulation commonly used in standard hand-eye calibration algorithms. The proposed method is based on a nonlinear optimization problem, so that the estimation problem can be solved efficiently and robustly, and can be easily extended to different camera-robot setups (e.g., eye-on-base or eye-in-hand). An extensive evaluation based on simulated and real experiments has been performed, proving its good estimation accuracy in terms of reprojection error. The experimental results with real robots show that the proposed method is applicable to relevant industrial contexts and improves the quality and precision of the camera-robot transformation estimation with respect to state-of-the-art approaches.

Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes a hybrid approach that combines geometric and appearance features for training Deep Encoder-Decoder architectures to detect the traversability score in real urban contexts. The proposed approach has been tested with two Deep Learning architectures on a public dataset of outdoor driving scenarios. Thanks to our approach, we are able to reach high levels of accuracy in detecting the correct traversability score in environments of highly variable complexity. This demonstrates the effectiveness and robustness of the proposed method.

In this work we present an industrial system for the inspection of composite parts in the aerospace industry, based on X-ray sensors and robotic manipulators. Such system is designed to identify any type of defects such as, missing gluing, core cell deformation, cracks or foreign objects, which may occur between layers of which these objects are composed. The inspection process involves back-projection of X-ray images onto the 3D CAD model of the inspected part, to directly locate the defects on the part itself. The complete system has been implemented in a real industrial workcell that involves two synchronized robots equipped with a X-ray source-detector system. The two robots move autonomously along a pre-computed trajectory without any human intervention, and the back-projection of the acquired images is efficiently performed at run-time using the proposed algorithm. The experiments demonstrate that the X-ray images back-projection is successful and can effectively replace standard manually guided inspections. This has a high impact on the factory automation cycle since it helps to reduce the effort and time needed for each inspection task. This work is part of a EU funded project called SPIRIT.