We propose a novel angular velocity estimation method to increase the robustness of Simultaneous Localization And Mapping (SLAM) algorithms against gyroscope saturations induced by aggressive motions. Field robotics expose robots to various hazards, including steep terrains, landslides, and staircases, where substantial accelerations and angular velocities 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 resilience of the SLAM framework is oftentimes given little consideration. 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 way to estimate the angular velocity using accelerometers during extreme rotations caused by tumbling. We show that our method reduces the median localization error by 71.5 % in translation and 65.5 % in rotation and reduces the number of SLAM failures by 73.3 % on the collected data. We also propose the Tumbling-Induced Gyroscope Saturation (TIGS) dataset, which consists of outdoor experiments recording the motion of a lidar subject to angular velocities four times higher than other available datasets. The dataset is available online at https://github.com/norlab-ulaval/Norlab_wiki/wiki/TIGS-Dataset.

翻译：我们提出一种新颖的角速度估计方法，以增强同步定位与地图构建（SLAM）算法在剧烈运动引起的陀螺仪饱和场景下的鲁棒性。户外机器人面临多种危险环境，包括陡峭地形、滑坡和楼梯，当机器人失去稳定性发生翻滚时，会产生巨大的加速度和角速度。这种极端运动可能导致传感器测量值饱和，尤其是陀螺仪——这是最先失效的传感器。尽管机器人结构完整性面临风险，但SLAM框架的恢复能力却常被忽视。因此，即使机器人物理层面能继续执行任务，其运行也会因世界表征受损而受到破坏。针对此问题，我们提出一种利用加速度计在翻滚引起的极端旋转期间估计角速度的方法。实验表明，该方法将定位中位误差降低了71.5%（平移）和65.5%（旋转），并将SLAM故障次数减少了73.3%。我们还提出了翻滚诱发陀螺仪饱和（TIGS）数据集，该数据集包含户外实验记录，所记录的激光雷达运动角速度是现有其他数据集的四倍。数据集可在https://github.com/norlab-ulaval/Norlab_wiki/wiki/TIGS-Dataset 获取。