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 https://fusionportable.github.io/dataset/fusionportable_v2.

翻译：同时定位与地图构建（SLAM）技术已广泛应用于从救援作业到自动驾驶的各类机器人场景。然而，SLAM算法的泛化性仍面临重大挑战，现有数据集在平台与环境可扩展性方面普遍不足。为解决这一局限，我们提出FusionPortableV2——一个具有显著传感器多样性、丰富运动模式及广泛环境场景的多传感器SLAM数据集。该数据集包含27个序列，总时长超过2.5小时，采集自四种不同平台：手持式套装、轮式机器人、足式机器人与车辆。这些序列覆盖建筑、校园及城区等多样化场景，总里程达38.7公里。此外，数据集提供了地面真值轨迹及覆盖约0.3平方公里的RGB点云地图。为验证本数据集对推动SLAM研究的价值，我们评估了多种最先进（SOTA）SLAM算法。进一步地，通过探索数据集在单目深度估计领域的应用潜力，我们展示了其超越传统SLAM任务的广泛适用性。完整数据集（含传感器数据、地面真值及标定细节）可通过https://fusionportable.github.io/dataset/fusionportable_v2获取。