ROOAD：RELLIS越野里程分析数据集
ROOAD: RELLIS Off-road Odometry Analysis Dataset
摘要：
视觉惯性里程计（VIO）的开发和实施主要集中在结构化环境中，但对越野环境中的定位的兴趣正在增长。在本文中，我们提出了RELLIS越野里程分析数据集（ROOAD），它提供了高质量的，时间同步的越野单目视觉惯性数据序列，以进一步发展相关研究。我们在两种最先进的VIO算法上评估了数据集，（1）Open-VINS和（2）VINS-Fusion。我们的研究结果表明，这两种算法在ROOAD数据集上的性能比在结构化环境中的性能差2到30倍。此外，OpenVINS在越野环境中具有比VINS-Fusion更好的跟踪稳定性和实时性能，而VINS-Fusion在几个数据序列中的跟踪精度优于OpenVINS。由于Kalibr工具包中的相机-IMU校准工具在这项工作中得到了广泛的使用，因此我们包含了几个校准数据序列。我们的手动测量显示Kalibr的工具在相机和IMU之间的相机框架中的方位误差达到+/-1度，最好为+/-1 mm（x轴和y轴），最差为+/-10 mm（z轴）。这个新的数据集为研究人员设计和测试他们的定位算法提供了一组新的场景，以及对VIO在越野环境中当前性能的重要见解。ROOAD数据集：github.com/unmannedlab/ROOAD
英文摘要：
The development and implementation of visual-inertial odometry (VIO) has focused on structured environments, but interest in localization in off-road environments is growing. In this paper, we present the RELLIS Off-road Odometry Analysis Dataset (ROOAD) which provides high-quality, time-synchronized off-road monocular visual-inertial data sequences to further the development of related research. We evaluated the dataset on two state-of-the-art VIO algorithms, (1) Open-VINS and (2) VINS-Fusion. Our findings indicate that both algorithms perform 2 to 30 times worse on the ROOAD dataset compared to their performance in structured environments. Furthermore, OpenVINS has better tracking stability and real-time performance than VINS-Fusion in the off-road environment, while VINS-Fusion outperformed OpenVINS in tracking accuracy in several data sequences. Since the camera-IMU calibration tool from Kalibr toolkit is used extensively in this work, we have included several calibration data sequences. Our hand measurements show Kalibr's tool achieved +/-1 degree for orientation error and +/-1 mm at best (x- and y-axis) and +/-10 mm (z-axis) at worse for position error in the camera frame between the camera and IMU. This novel dataset provides a new set of scenarios for researchers to design and test their localization algorithms on, as well as critical insights in the current performance of VIO in off-road environments. ROOAD Dataset: github.com/unmannedlab/ROOAD
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
