3D point cloud and RGBD of pedestrians in robot crowd navigation: detection and tracking

The current dataset – crowdbot – presents outdoor pedestrian tracking from onboard sensors on a personal mobility robot navigating in crowds. The robot Qolo, a personal mobility vehicle for people with lower-body impairments was equipped with a reactive navigation control operating in shared-control or autonomous mode when navigating on three different streets of the city of Lausanne, Switzerland during farmer’s market days and Christmas market. Full Dataset here: DOI:10.21227/ak77-d722The dataset includes point clouds from a frontal and rear 3D LIDAR (Velodyne VLP-16) at 20 Hz, and a frontal facing RGBD camera (Real Sense D435). The data comprise over 250k frames of recordings in crowds from light densities of 0.1 ppsm to dense crowds of over 1.0 ppsm. We provide the robot state of pose, velocity, and contact sensing from Force/Torque sensor (Botasys Rokubi 2.0).We provide the metadata of people detection and tracking from onboard real-time sensing (DrSPAAM detector), people class labelled 3D point cloud (AB3DMOT), estimated crowd density, proximity to the robot, and path efficiency of the robot controller (time to goal, path length, and virtual collisions).One recording of the dataset includes approximately 120s of data in rosbag format for Qolo’s sensors, as well as, data in npy format for easy read and access. All code for meta data processing and extraction of the raw files is provided open access: epfl-lasa/crowdbot-evaluation-tools 250k frames of data – over 200 minutes of recordingsQolo Robot state: localization – pose – velocity – controller state2 x 3D point cloud (VLP-16)1x RGBD image and depth camera (Realsense D435)3 x people detectors (DrSPAAM, Yolo)1x Tracker Contact Forces (Botasys Rokubi 2.0)Note: current data do not contain the RGBD images as they are being processed for face blurring. Nontheless, Yolo output of people detections are included. The rgbd image with bouding boxes will be uploaded as soon as the process is completed.Cite as: Paez-Granados D., Hen Y., Gonon D., Huber L., & Billard A., (2021), “3D point cloud and RGBD of pedestrians in robot crowd navigation: detection and tracking.”, Dec. 2021. IEEE Dataport, doi: https://dx.doi.org/10.21227/ak77-d722.

当前数据集——Crowdbot——展示了在人群中导航的移动机器人上的车载传感器进行户外行人跟踪。Qolo机器人，一种专为下肢残疾者设计的个人移动车辆，配备有反应式导航控制，在瑞士洛桑市的不同街道上，在农民市场日和圣诞节市场期间以共享控制或自主模式进行导航。完整数据集请参考此处：DOI：10.21227/ak77-d722。该数据集包括来自正面和后方的3D激光雷达（Velodyne VLP-16）在20赫兹下的点云数据，以及一个面向前方的RGBD相机（Real Sense D435）的数据。数据包含超过25万帧的记录，从0.1 ppsm的光密度到超过1.0 ppsm的密集人群。我们提供了机器人状态、姿态、速度和接触传感（Botasys Rokubi 2.0力矩传感器）的传感器数据。我们还提供了基于车载实时感知（DrSPAAM检测器）的人检测和跟踪元数据、标记的人类类别3D点云（AB3DMOT）、估计的人群密度、与机器人的距离以及机器人控制器路径效率（到达目标时间、路径长度和虚拟碰撞）。数据集的一次记录包括大约120秒的rosbag格式下的Qolo传感器数据，以及易于读取和访问的npy格式数据。所有元数据处理和原始文件提取的代码均以开源方式提供：epfl-lasa/crowdbot-evaluation-tools。25万帧数据——超过200分钟的记录。Qolo机器人状态：定位——姿态——速度——控制器状态2个3D点云（VLP-16）1个RGBD图像和深度相机（Realsense D435）3个行人检测器（DrSPAAM，Yolo）1个接触力追踪（Botasys Rokubi 2.0）注意：当前数据不包含RGBD图像，因为它们正在处理以进行面部模糊化。尽管如此，仍包括Yolo行人检测输出。一旦处理完成，带有边界框的RGBD图像将被上传。引用格式：Paez-Granados D.，Hen Y.，Gonon D.，Huber L.，& Billard A.，（2021），“在机器人人群中导航的行人3D点云和RGBD：检测与跟踪”，2021年12月。IEEE Dataport，doi：https://dx.doi.org/10.21227/ak77-d722。