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MPL Dataset Toolbox

VECtor Benchmark is the first complete set of benchmark datasets captured with a multi-sensor setup containing an event-based stereo camera, a regular stereo camera, multiple depth sensors, and an inertial measurement unit. The setup is fully hardware-synchronized and underwent accurate extrinsic calibration. All sequences come with ground truth data captured by highly accurate external reference devices such as a motion capture system. Individual sequences include both small and large-scale environments, and cover the specific challenges targeted by dynamic vision sensors.

This toolbox is a ROS workspace integrating with a set of easy-to-use dataset functions, including:

Event Visualizer: convert raw event stream into accumulated event frames.
Data Validator: validate the downloaded rosbag or the merged rosbag for its completeness.
Bag Merger: merge multiple, single-topic rosbags chronologically into one complete rosbag.
Bag Splitter: split one multi-topic rosbag into a few compressed, single-topic rosbags.
Timeline Reconstructor: reconstruct raw recording's timeline for each data channel.
Bag-to-HDF5 Converter: convert event stream(s) into compressed HDF5 file(s) under DSEC format.
Bag-to-Rawfile Converter: convert all other data stream(s) into raw file(s).
CSV-to-TUM Converter: convert OptiTrack readings into text file under TUM format.

License and Citation

This toolbox, together with the MPL Calibration Toolbox and the k4a Projector, is available as open-source under the terms of the BSD-3-Clause-Clear License. If you use this toolbox in an academic context, please cite the publication as follows:

@Article{gao2022vector,
  author  = {Gao, Ling and Liang, Yuxuan and Yang, Jiaqi and Wu, Shaoxun and Wang, Chenyu and Chen, Jiaben and Kneip, Laurent},
  title   = {{VECtor}: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM},
  journal = {IEEE Robotics and Automation Letters},
  pages   = {8217--8224},
  volume  = {7},
  number  = {3},
  year    = {2022},
  doi     = {10.1109/LRA.2022.3186770}
}

Getting Started

The following instructions are tested on (A) Ubuntu 18.04 with ROS Melodic, and (B) Ubuntu 20.04 with ROS Noetic. A ROS desktop-full installation is required. On top of that, the following libraries (Eigen 3, OpenCV, Point Cloud Library) have to be installed:

sudo apt-get update
sudo apt-get install libeigen3-dev libopencv-dev libpcl-dev
ln -s /usr/include/eigen3/Eigen /usr/include/Eigen

After that, enter your catkin workspace and the build can be triggered with the following command:

cd ~/catkin_ws/src
git clone https://github.com/mgaoling/mpl_dataset_toolbox.git
cd ..
catkin_make
source ~/catkin_ws/devel/setup.bash

Event Visualizer

The event visualizer converts raw event stream into accumulated event frames. It can handle at most two different event streams.

For monocular setup, check and modify the parameters in the config/event_visualizer.yaml, then launch the visualization by:

roslaunch mpl_dataset_toolbox event_visualization.launch

For stereo setup, check and modify the parameters in the config/event_stereo_visualizer.yaml, then launch the visualization by:

roslaunch mpl_dataset_toolbox event_stereo_visualization.launch

To visualize the rosbag, run rqt or rqt_image_view in another terminal to display the accumulated event frames.

Data Validator

The data validator helps to check the correctness and completeness of a downloaded, single-topic rosbag or a merged rosbag. It also calculates the Mean Event Rate if the event stream exists.

Launch the validation in one terminal by:

roslaunch mpl_dataset_toolbox data_validation.launch

Play the rosbag in another terminal by:

rosbag play [data.bag]

Terminate the validation once the playback is over.

Note: Dropped frames on Kinect stream are to be expected, owing to the singularity of the depth calculation during recording. We have deliberately left a blank on the depth stream during the timeline reconstruction, to maintain the overall correctness.

Bag Merger

Bag merger helps to merge different single-topic rosbags chronologically. Note that data sequences with different prefixes can be safely stored under the same directory. DO NOT MODIFY THE FILENAME!

Put all single-topic rosbags under one folder, then launch the bag merger by:

roslaunch mpl_dataset_toolbox bag_merger.launch directory_path:=[path_to_directory]

Note: Here, only an absolute path without ~, or a relative path that is relative to this toolbox is allowed for the directory path.

Bag Splitter

Bag Splitter helps to split a multi-topic rosbag into a few single-topic rosbags, with a compression option.

Launch the bag splitter by:

roslaunch mpl_dataset_toolbox bag_splitter.launch bag_path:=[data.bag] need_compression:=[true_or_false]

Note: Here, only an absolute path without ~, or a relative path that is relative to this toolbox is allowed for the rosbag path.

Tool Kit

Timeline Reconstructor

roslaunch mpl_dataset_toolbox timeline_reconstruction.launch bag_path:=[data.bag] gt_path:=[ground_truth.csv]

Bag-to-HDF5 Converter

roscd mpl_dataset_toolbox/script
python3 bag2hdf5.py [data.bag]

Bag-to-Rawfile Converter

roslaunch mpl_dataset_toolbox bag2rawfile.launch bag_path:=[data.bag]

CSV-to-TUM Converter

roscd mpl_dataset_toolbox/script
python3 csv2tum.py [ground_truth.csv] [data.bag]