A Flexible Framework for Mobile Robot Pose Estimation and Multi-Sensor Self-Calibration

The design and the development of the position and orientation tracking system of a mobile robot, and the calibration of its sensor parameters (e.g., displacement, misalignment and iron distortions), are challenging and time consuming tasks in every autonomous robotics project. The ROAMFREE framework delivers turn-on-and-go multi-sensors pose tracking and self-calibration modules and it is designed to be flexible and to adapt to every kind of mobile robotic platform. In ROAMFREE, the sensor data fusion problem is formulated as a hyper-graph optimization where nodes represent poses and calibration parameters and edges the non-linear measurement constraints. This formulation allows us to solve both the on-line pose tracking and the off-line sensor self-calibration problems. In this paper, we introduce the approach and we discuss a real platform case study, along with experimental results.