This article presents a visual–inertial dataset gathered in indoor and outdoor scenarios with a handheld custom sensor rig, for over 80 min in total. The dataset contains hardware-synchronized data from a commercial stereo camera (Bumblebee®2), a custom stereo rig, and an inertial measurement unit. The most distinctive feature of this dataset is the strong presence of low-textured environments and scenes with dynamic illumination, which are recurrent corner cases of visual odometry and simultaneous localization and mapping (SLAM) methods. The dataset comprises 32 sequences and is provided with ground-truth poses at the beginning and the end of each of the sequences, thus allowing the accumulated drift to be measured in each case. We provide a trial evaluation of five existing state-of-the-art visual and visual–inertial methods on a subset of the dataset. We also make available open-source tools for evaluation purposes, as well as the intrinsic and extrinsic calibration parameters of all sensors in the rig. The dataset is available for download at http://mapir.uma.es/work/uma-visual-inertial-dataset

中文翻译：

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UMA-VI 数据集：低纹理和动态照明环境中的视觉惯性里程计

本文展示了使用手持式定制传感器装备在室内和室外场景中收集的视觉惯性数据集，总共超过 80 分钟。该数据集包含来自商用立体相机 (Bumblebee®2)、定制立体设备和惯性测量单元的硬件同步数据。该数据集最显着的特征是具有动态照明的低纹理环境和场景的强烈存在，它们是视觉里程计和同时定位和映射 (SLAM) 方法的反复出现的极端情况。该数据集包含 32 个序列，并在每个序列的开头和结尾提供了真实姿势，从而允许在每种情况下测量累积漂移。我们在数据集的一个子集上对五种现有的最先进的视觉和视觉惯性方法进行了试验评估。我们还提供用于评估目的的开源工具，以及钻机中所有传感器的内在和外在校准参数。数据集可在 http://mapir.uma.es/work/uma-visual-inertial-dataset 下载