Continuous Integration and Continuous Deployment are known methodologies for software development that increase the overall quality of the development process. Several robotic software repositories make use of CI/CD tools as an aid to development. However, very few CI pipelines take advantage of using cloud computing to run simulations. Here, a CI pipeline is proposed that takes advantage of such features, applied to the development of ATOM, a ROS-based application capable of carrying out the calibration of generalized robotic systems. The proposed pipeline uses GitHub Actions as a CI/CD engine, AWS RoboMaker as a service for running simulations on the cloud and Rigel as a tool to both containerize ATOM and execute the tests. In addition, a static analysis and unit testing component is implemented with the use of Codacy. The creation of the pipeline was successful, and it was concluded that it constitutes a valuable tool for the development of ATOM and a blueprint for the creation of similar pipelines for other robotic systems.

For any robotic application, accurate sensor calibration is crucial. In the case of mobile platforms or flying drones, a sensor commonly utilized is the Inertial Measurement Unit (IMU). Current approaches to the calibration of IMU-equipped robotic systems focus on sensor-to-sensor calibration, meaning a second sensor is necessary for the calibration process. Furthermore, a great number of those rely on integrating the sensor measurements to obtain its pose, which leads to integration errors. In this work, we present a method for the extrinsic calibration of IMUs in robotic systems, which avoids the errors originating from IMU integration by instead taking a derivative approach using Savitzky–Golay filters. The proposed calibration method estimates the transformation between an IMU sensor and its parent frame in the system’s kinematic chain by minimizing the differences between derived linear accelerations and angular velocities and those measured by the sensor. Simulated data is used to establish a ground truth against which the calibration results are compared. Results indicate that the proposed method achieves a higher accuracy than the alternatives it is compared against, while also showing the method can be applied to industrial-grade IMUs.

The calibration of mobile manipulators requires accurate estimation of both the transformations provided by the localization system and the transformations between sensors and the motion coordinate system. Current works offer limited flexibility when dealing with mobile robotic systems with many different sensor modalities. In this work, we propose a calibration approach that simultaneously estimates these transformations, enabling precise calibration even when the localization system is imprecise. This approach is integrated into Atomic Transformations Optimization Method (ATOM), a versatile calibration framework designed for multi-sensor, multi-modal robotic systems. By formulating calibration as an extended optimization problem, ATOM estimates both sensor poses and calibration pattern positions. The proposed methodology is validated through simulations and real-world case studies, demonstrating its effectiveness in improving calibration accuracy for mobile manipulators equipped with diverse sensor modalities.