Framework for Fast Experimental Testing of Autonomous Navigation Algorithms

Abstract

Research in mobile robotics requires fully operative autonomous systems to test and compare algorithms in real-world conditions. However, the implementation of such systems remains to be a highly time-consuming process. In this work, we present an robot operating system (ROS)-based navigation framework that allows the generation of new autonomous navigation applications in a fast and simple way. Our framework provides a powerful basic structure based on abstraction levels that ease the implementation of minimal solutions with all the functionalities required to implement a whole autonomous system. This approach helps to keep the focus in any sub-problem of interest (i.g. localization or control) while permitting to carry out experimental tests in the context of a complete application. To show the validity of the proposed framework we implement an autonomous navigation system for a ground robot using a localization module that fuses global navigation satellite system (GNSS) positioning and Monte Carlo localization by means of a Kalman filter. Experimental tests are performed in two different outdoor environments, over more than twenty kilometers. All the developed software is available in a GitHub repository.

Keywords

• autonomous navigation
• mobile robots
• Monte Carlo localization
• SLAM
• GNSS
• planning
• control
• Kalman filter