3D point cloud and RGBD of pedestrians in robot crowd navigation: detection and tracking
- Citation Author(s):
- Submitted by:
- Diego Felipe Pa...
- Last updated:
- Fri, 12/30/2022 - 04:08
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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-d722
The 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 recordings
- Qolo Robot state: localization – pose – velocity – controller state
- 2 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.
Download the data files and put them under data (place the uncompressed files or symbolic links) cd path/to/crowdbot_tools/data # (recommended) create symbolic links of rosbag folder instead of copying data: ln -s /data_qolo/lausanne_2021/24_04_2021/shared_control 0424_shared_control ln -s /data_qolo/lausanne_2021/24_04_2021/RDS/detector 0424_rds_detector
The used file structure is as follows:
qolo/: codespace for crowdbot evaluation
notebook/: example notebooks for demo
sh_scripts/: shell scripts to execute pipelines for extracting source data, applying algorithms, and evaluating with different metrics
Examples of how to access the data can be found in the open repository: https://github.com/epfl-lasa/crowdbot-evaluation-tools
- NPY - Full dataset with all LIDAR, metadata, and processed data crowdbot_dataset.zip (58.30 GB)
- Example-1: Metadata from 13 recordings of shared control navigation on a dense crowd (1 ppsm) 1203_shared_control_processed.zip (173.29 MB)
- Example-1:Raw 3D LIDAR (x2) data 1203_shared_control_lidars.zip (7.16 GB)
- Example-2: Raw 3D LIDAR (x2) data 1203_manual_lidars.zip (1.33 GB)
- Example-2: Metadata from 2 recordings during manual driving in a dense crowd 1203_manual_processed.zip (31.97 MB)
- Onboard_RGBD_video (x2) 1203_sample_raw_video_2x.mp4 (14.14 MB)
- rosbags_25_03_rds no_rbgd lidar_rosbags_0325_rds.zip (10.50 GB)
- rosbags_25_03_sc no_rbgd lidar_rosbags_0325_shared_control.zip (7.08 GB)
- rosbags_27_03_sc no_rbgd lidar_rosbags_0327_shared_control.zip (28.74 GB)
- rosbags_10_04_mds no_rbgd lidar_rosbags_0410_mds.zip (13.52 GB)
- rosbags_10_04_rds no_rbgd lidar_rosbags_0410_rds.zip (18.84 GB)
- rosbags_10_04_sc no_rbgd lidar_rosbags_0410_shared_control.zip (11.84 GB)
- rosbags_24_04_mds no_rbgd lidar_rosbags_0424_mds.zip (7.99 GB)
- rosbags_24_04_rds no_rbgd lidar_rosbags_0424_rds.zip (6.79 GB)
- rosbags_24_04_sc no_rbgd lidar_rosbags_0424_shared_control.zip (7.99 GB)
- rosbags_03_12_manual no_rbgd lidar_rosbags_1203_manual.zip (1.61 GB)
- rosbags_03_12_sc no_rbgd lidar_rosbags_1203_shared_control.zip (14.04 GB)
- rosbags_03_12_manual-rgbd_defaced rosbags_03_12_manual-rgbd_defaced.zip (8.98 GB)
- rosbags_10_04_sc-rgbd_defaced rosbags_10_04_shared_control-rgbd_defaced.zip (27.42 GB)
- rosbags_10_04_mds-rgbd_defaced rosbags_10_04_mds-rgbd_defaced.zip (36.83 GB)
- rosbags_25_03_sc-rgbd_defaced rosbags_25_03_shared_control-rgbd_defaced.zip (15.29 GB)
- rosbags_24_04_sc-rgbd_defaced rosbags_24_04_shared_control-rgbd_defaced.zip (22.85 GB)
- rosbags_24_04_mds-rgbd_defaced rosbags_24_04_mds_rgbd_defaced.zip (17.44 GB)
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