Pose Tracking and Sensor Self-Calibration for an All-terrain Autonomous Vehicle

In this work we address the simultaneous pose tracking and sensor self-calibration problem by applying a pose-graph optimization approach. A factor-graph is employed to store robot pose estimates at different time instants and calibration parameters such as magnetometer hard and soft iron distortion and gyroscope bias. Specific factors are developed in this paper to handle Ackermann kinematic readings, inertial measurement units, magnetometers and global positioning systems. An experimental evaluation supports the viability of the approach considering an autonomous all-terrain vehicle, for which we perform calibration and real-time pose tracking during navigation.