Abstract
This study presents a comprehensive multi-sensor dataset designed for 3D mapping in challenging indoor and outdoor environments. The dataset comprises data from infrared cameras, depth cameras, LiDAR, and 4D millimeter-wave radar, facilitating exploration of advanced perception and mapping techniques. Integration of diverse sensor data enhances perceptual capabilities in extreme conditions such as rain, snow, and uneven road surfaces. The dataset also includes interactive robot data at different speeds indoors and outdoors, providing a realistic background environment. Slam comparisons between similar routes are conducted, analyzing the influence of different complex scenes on various sensors. Various SLAM algorithms are employed to process the dataset, revealing performance differences among algorithms in different scenarios. In summary, this dataset addresses the problem of data scarcity in special environments, fostering the development of perception and mapping algorithms for extreme conditions. Leveraging multi-sensor data including infrared, depth cameras, LiDAR, 4D millimeter-wave radar, and robot interactions, the dataset advances intelligent mapping and perception capabilities.Our dataset is available at this https URL.
Abstract (translated)
本研究旨在为具有挑战性的室内和室外环境3D建模创建一个全面的传感器数据集。数据集包括来自红外摄像机、深度相机、激光雷达和4D毫米波雷达的数据，促进了先进感知和建模技术的探索。 diverse传感器数据的集成增强了在极端条件下的感知能力，例如雨、雪和不平整的路面。数据集中的室内和室外交互式机器人数据以不同速度运行，提供了一个真实的背景环境。在类似路线的SLAM比较中进行了研究，分析了不同场景下各种复杂场景对各种传感器的影響。采用各种SLAM算法处理数据，揭示了不同情景中算法之间性能的差异。总之，这个数据集解决了在特殊环境中的数据稀缺问题，推动了为极端情况下的感知和建模算法的发展。通过利用包括红外、深度相机、激光雷达、4D毫米波雷达和机器人交互在内的多传感器数据，数据集提高了智能建模和感知能力。我们的数据集可在以下链接处获得：https://www.example.com。
URL
https://arxiv.org/abs/2404.09622
