Abstract
Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring notable sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises $27$ sequences, spanning over $2.5$ hours and collected from four distinct platforms: a handheld suite, wheeled and legged robots, and vehicles. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of $38.7km$. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately $0.3km^2$. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad applicability beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The complete dataset, including sensor data, GT, and calibration details, is accessible at this https URL.
Abstract (translated)
同时定位与映射（SLAM）技术已经在各种机器人场景中得到了广泛应用，从救援行动到自动驾驶。然而，SLAM算法的泛化仍然是一个重要的挑战，因为当前的数据集在平台和环境方面缺乏可扩展性。为了解决这个问题，我们提出了FusionPortableV2，一个包含显著传感器多样性、多样运动模式和广泛环境场景的多传感器SLAM数据集。我们的数据集包括4个不同平台的27个序列，总长度超过2.5小时，来自四个不同的平台：手持设备套装、轮式和腿式机器人以及车辆。这些序列涵盖了各种场景，包括建筑物、校园和城市地区，总长度为38.7公里。此外，数据集还包括地面真实（GT）轨迹和覆盖约0.3平方公里的RGB点云地图。为了验证我们数据在推动SLAM研究方面的实用性，我们评估了几种最先进的（SOTA）SLAM算法。此外，我们通过研究其潜在的单目深度估计能力，证明了数据集在传统SLAM任务之外的广泛应用。完整的数据集，包括传感器数据、GT和校准细节，可以通过此链接访问：https://www.example.com/
URL
https://arxiv.org/abs/2404.08563
