Tsukuba Challenge 2017 Dynamic Object Tracks Dataset for Pedestrian Behavior Analysis
Tsukuba Challenge 2017 Dynamic Object Tracks Dataset for Pedestrian Behavior Analysis
复制标题
筑波挑战赛 2017 用于行人行为分析的动态物体轨迹数据集
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
K. Takeda
中科院分区：
文献类型：
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作者：
Jacob Lambert;Leslie Liang;Luis Yoichi Morales;Naoki Akai;Alexander Carballo;E. Takeuchi;Patiphon Narksri;Shunya Seiya;K. Takeda
在没有交通规则的情况下，在社会环境中导航是一年一度的筑波挑战赛的核心任务。在这种背景下，更好地理解影响社会动态的软规则是改进机器人导航的关键。以前的研究试图通过微观互动来模拟社会行为，但由此产生的涌现行为在很大程度上依赖于初始条件，特别是宏观环境。因此，对固定环境中行人行为的数据驱动研究可能会提供对这一宏观方面的关键洞察，但适当的数据几乎不可用。为了支持这一研究流，我们发布了一个开源的动态对象轨迹数据集，该数据集定位于2017筑波挑战环境的地图中。配备了激光雷达、相机、IMU和里程计的数据采集平台反复导航挑战的过程，记录路人的观察。使用背景地图，我们在环境中定位自己，从点云数据中去除静态背景，将剩余的点聚集成动态对象，并跟踪它们随时间的移动。在这项工作中，我们提出了筑波挑战动态对象轨迹数据集，其中包含近10,000条行人、骑自行车的人和其他动态智能体的轨迹，特别是自主机器人。我们提供了一张环境的3D地图，用作所有轨迹的全局框架。对于每个轨迹，我们以固定的时间间隔提供估计的位置、速度、航向和旋转速度，以及对象和分段激光雷达点云的边界框。作为补充，我们提供了一个讨论，集中在数据中的一些可辨别的宏观模式。
Navigation in social environments, in the absence of traffic rules, is the difficult task at the core of the annual Tsukuba Challenge. In this context, a better understanding of the soft rules that influence social dynamics is key to improve robot navigation. Prior research attempts to model social behavior through microscopic interactions, but the resulting emergent behavior depends heavily on the initial conditions, in particular the macroscopic setting. As such, data-driven studies of pedestrian behavior in a fixed environment may provide key insight into this macroscopic aspect, but appropriate data is scarcely available. To support this stream of research, we release an open-source dataset of dynamic object trajectories localized in a map of 2017 Tsukuba Challenge environment. A data collection platform equipped with lidar, camera, IMU, and odometry repeatedly navigated the challenge’s course, recording observations of passersby. Using a background map, we localized ourselves in the environment, removed the static background from the point cloud data, clustered the remaining points into dynamic objects and tracked their movements over time. In this work, we present the Tsukuba Challenge Dynamic Object Tracks dataset, which features nearly 10,000 trajectories of pedestrians, cyclists, and other dynamic agents, in particular autonomous robots. We provide a 3D map of the environment used as global frame for all trajectories. For each trajectory, we provide at regular time intervals an estimated position, velocity, heading, and rotational velocity, as well as bounding boxes for the objects and segmented lidar point clouds. As additional contribution, we provide a discussion which focuses on some discernible macroscopic patterns in the data.
影响因子：
3.5
作者：
Hornung, Armin;Wurm, Kai M.;Burgard, Wolfram
通讯作者：
Burgard, Wolfram
影响因子：
9.2
作者：
Geiger, A.;Lenz, P.;Urtasun, R.
通讯作者：
Urtasun, R.