Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions

We propose a novel angular velocity estimation method to increase the robustness of Simultaneous Localiza- tion And Mapping (SLAM) algorithms against gyroscope sat- urations induced by aggressive motions. Field robotics expose robots to various hazards, including steep terrains, landslides, and staircases, where substantial accelerations and angular ve- locities can occur if the robot loses stability and tumbles. These extreme motions can saturate sensor measurements, especially gyroscopes, which are the first sensors to become inoperative. While the structural integrity of the robot is at risk, the robust- ness of the SLAM framework is oftentimes given little consid- eration. Consequently, even if the robot is physically capable of continuing the mission, its operation will be compromised due to a corrupted representation of the world. Regarding this problem, we propose a method to estimate the angular velocity using accelerometers during extreme rotations caused by tum- bling. We show that our method reduces the median localization error by 71.5 % in translation and 65.5 % in rotation and is robust to mapping failures, which occurred in 37.5 % of the ex- periments without our method. We also propose the Tumbling- Induced Gyroscope Saturation (TIGS) dataset, which consists of outdoor experiments recording the motion of a mechani- cal lidar subject to angular velocities four times higher than other similar datasets available. The dataset is available on- line at https://github.com/norlab-ulaval/Norlab_ wiki/wiki/TIGS-Dataset.