This article proposes a general approach to solve the hand–eye calibration problem. The system is general since it is able to calibrate any number of cameras and, moreover, is able to simultaneously perform the calibration of several instances of the two common hand–eye calibration use cases: eye-on-hand and eye-to-base. The calibration is solved with a nonlinear least squares method, and the reprojection error is used as a metric to guide the optimization procedure. Our approach is seamlessly integrated with the robot operating system framework and allows for the interactive positioning of sensors and labeling of data, facilitating both the data acquisition and labeling and the calibration procedures. Results show that the proposed approach is able to handle any calibration use case with a minimal initial configuration. The approach is compared with several other state-of-the-art hand–eye calibration algorithms. Results show that the proposed approach produces very accurate calibrations when compared to the state of the art.

This paper presents a fast scan matching approach to mobile robot localization supported by a continuous likelihood field. The likelihood field plays a central role in the approach, as it avoids the necessity to establish direct correspondences; it is the connection link between scan matching and robotic localization, and it provides a reduced computational complexity. Scan matching is formulated as a non-linear least squares problem and solved by the Gauss-Newton and Levenberg-Marquardt methods. Furthermore, to reduce the influences of outliers during optimization, a loss function is introduced. The proposed solution was evaluated using a publicly available dataset and compared with AMCL, a state-of-the-art localization algorithm. Our proposal shows to be a fast and accurate localization algorithm suitable for any type of operation.

The adoption of robotics technology has the potential to advance quality, efficiency and safety for manufacturing enterprises, in particular small and medium-sized enterprises. This paper presents a human-robot interaction (HRI) system that enables a robot to receive commands, provide information to a human teammate and ask them a favor. In order to build a robust HRI system based on gesture recognition, three key issues are addressed: richness, multiple feature fusion and failure verification. The developed system has been tested and validated in a realistic lab with a real mobile manipulator and a human teammate to solve a puzzle game.