Subterranean positioning for a semi-autonomous robot supporting emergency task forces

This paper proposes a positioning algorithm for a semi-autonomous robot in subterranean scenarios. The robot is equipped with positioning sensors, imaging sensors, and sensors to detect hazardous materials. The sensors can be used to automatically generate a site map to increase safety for emergency forces. To create an accurate map, the position and attitude of the robot have to be determined. This is done using an extended Kalman filter which fuses data from LIDAR, wheel odometry, and a MEMS IMU. Tests were carried out in a tunnel in Eisenerz, Austria. To evaluate the achievable accuracy, the estimated position of the filter is compared to a ground truth. The results show that with the developed sensor fusion algorithm, a horizontal positioning error of 1.07% of the traveled distance can be achieved.