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<span style="font-weight: 600;">The ECMD Datasets</span>
</h1>
<p class="desc">
An Event-Centric Multisensory Driving Dataset for SLAM.
</p>
</header>
<article>
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<p><br></p>
<p style="text-align: justify;">
This is first event-based SLAM datasets specifically focus on urbanized autonomou driving.
We explore the inquiry: Are event cameras ready for autonomous driving?
At the same time, we also investigates the perceptual capabilities of various sensors, including LiDAR, standard cameras, infrared cameras, and GNSS-RTK/INS.
<!-- After the paper is accepted, we will release all the datasets and keep the dataset maintenance. -->
<br>
The contributions of our work can be summarized as follows:
</p>
<ul>
<li style="text-align: justify;"> Our <a href="/ecmd/sensors">sensor platform</a> consists of various novel sensors,
including two sets of stereo event cameras with distinct resolutions (640×480, 346×260),
an infrared camera,
stereo industrial cameras,
three mechanical LiDARs (including two slanted LiDARs),
an onboard inertial measurement unit (IMU),
and two global navigation satellite system (GNSS) receivers.
For the ground-truth, we adopt a centimeter-level position system that combines the GNSS real-time kinematic (RTK)
with the fiber optics gyroscope integrated inertial system as GNSS-RTK/INS.
</li>
<!-- <li style="text-align: justify;">
ALl sensors are <a href="/ecmd/calibration">well-calibrated</a> and temporally synchronized at the hardware level, with recording data simultaneously.
</li> -->
<li style="text-align: justify;"> ECMD collects 81 sequences covering over 200 kilometers of trajectories in various driving scenarios,
including dense streets, urban, tunnels, highways, bridges, and suburbs.
These <a href="/ecmd/download">sequences</a> are recorded under daylight and nighttime, providing challenging situations for Visual and LiDAR SLAM, e.g.,
dynamic objects, high-speed motion, repetitive scenarios, and HDR scenes.
Meanwhile, we evaluate existing state-of-the-art visual and LiDAR SLAM algorithms with various sensor modalities on our datasets.
Moreover, our dataset and benchmark results are released publicly available on our website.
</li>
<!-- <li style="text-align: justify;"> We present a comprehensive <a href="/ecmd/benchmark">benchmark</a> that evaluates existing state-of-the-art SLAM algorithms of various
sensor modalities and analyzes their limitations.
</li> -->
</ul>
<p style="text-align: justify;">
We hope that we can make some contributions for the development of event-based vision, especially event-based multi-sensor fusioin for autonomous driving.
<br>
The visualization of each sequence is available in <a href="/ecmd/download">Download section</a> and <a href="https://www.bilibili.com/video/BV1Km4y157KC/?spm_id_from=333.999.0.0&vd_source=a88e426798937812a8ffc1a9be5a3cb7">Bilibili</a>.
<br>
If you have any suggestions or questions, do not hesitate to propose an issue to our <a href="https://github.com/arclab-hku/ecmd">Github Repository</a>.
</p>
<p><br></p>
</div>
<!-- News -->
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<h2>News</h2>
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<table class="table table-sm table-borderless">
<tr>
<th scope="row">December 2, 2023</th>
<td>
We release our GNSS-RTK/INS gt and M8T/F9P GNSS at <a href="/ecmd/download">Download section</a>.
</td>
</tr>
<tr>
<th scope="row">November 28, 2023</th>
<td>
Our work has been accepted by IEEE Transactions on Intelligent Vehicles!
</td>
</tr>
<tr>
<th scope="row">November 21, 2023</th>
<td>
Calibration results and rosbag are avaliable at <a href="/ecmd/calibration">Calibration page</a>.
</td>
</tr>
<tr>
<th scope="row">November 19, 2023</th>
<td>
We release our sequences at <a href="/ecmd/download">Download section</a>.
</td>
</tr>
<tr>
<th scope="row">November 07, 2023</th>
<td>
The preprint version is available at <a href="https://arxiv.org/abs/2311.02327">arXiv</a>.
</td>
</tr>
<tr>
<th scope="row">October 31, 2023</th>
<td>
Watch our video presentation on <a href="https://www.bilibili.com/video/BV1pN411s79g/?spm_id_from=333.999.list.card_archive.click&vd_source=a88e426798937812a8ffc1a9be5a3cb7">Bilibili</a> or <a href="https://youtu.be/Q1F9M_DZLws">Youtube</a>.
</td>
</tr>
<tr>
<th scope="row">August 28, 2023</th>
<td>
We finish the evaluation of ECMD using various LiDAR SLAM (<a href="https://space.bilibili.com/499377825/channel/collectiondetail?sid=1835333">Record Videos</a>)
</td>
</tr>
<tr>
<th scope="row">August 22, 2023</th>
<td>
We complete the collection of all sequences (<a href="https://www.bilibili.com/video/BV1Km4y157KC/?share_source=copy_web&vd_source=f5ac2a23210d1b4b7b4aa5e374feae10">bilibili visulization</a>).
</td>
</tr>
<tr>
<th scope="row">June 2, 2023</th>
<td>
Driver code and time synchronization of event cameras are now available (<a href="https://github.com/arclab-hku/Event_based_VO-VIO-SLAM/tree/main/driver_code/dv-ros-master/script">Code</a>, <a href="https://www.bilibili.com/video/BV168411o7BJ/?spm_id_from=333.999.0.0&vd_source=a88e426798937812a8ffc1a9be5a3cb7">Bilibili</a>).
</td>
</tr>
<tr>
<th scope="row">June 1, 2023</th>
<td>
ECMD Datasets goes live!
</td>
</tr>
</table>
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<h2>BibTeX</h2>
Please cite the following publication when using this benchmark in an academic context:
<h2></h2>
<ol class="bibliography"><li>
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<div class="paper" style="text-align: justify;">P. Chen, W. Guan, F. Huang, Y. Zhong, W. Wen, L. Hsu, and P. Lu.
ECMD: An Event-Centric Multisensory Driving Dataset for SLAM.
IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, pp. 407-416, 2023.
</div>
<!-- Title -->
<!-- <div class="title" style="text-align: justify;">ECMD: An Event-Centric Multisensory Driving Dataset for SLAM.</div> -->
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IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, pp. 407-416, 2023.
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<a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
<a href="https://arxiv.org/abs/2311.02327" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">arXiv</a>
<a href="https://arclab-hku.github.io/ecmd/assets/pdf/2311.02327.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a>
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<p style="text-align: justify;">
Leveraging multiple sensors enhances complex environmental perception and increases resilience to varying luminance conditions and high-speed motion patterns, achieving precise localization and mapping.
This paper proposes, ECMD, an event-centric multisensory dataset containing 81 sequences and covering over 200 km of various challenging driving scenarios including high-speed motion, repetitive scenarios, dynamic objects, etc.
ECMD provides data from two sets of stereo event cameras with different resolutions (640×480, 346×260), stereo industrial cameras, an infrared camera, a top-installed mechanical LiDAR with two slanted LiDARs,
two consumer-level GNSS receivers, and an onboard IMU.
Meanwhile, the ground-truth of the vehicle was obtained using a centimeter-level high-accuracy GNSS-RTK/INS navigation system.
All sensors are well-calibrated and temporally synchronized at the hardware level, with recording data simultaneously.
We additionally evaluate several state-of-the-art SLAM algorithms for benchmarking visual and LiDAR SLAM and identifying their limitations.
<!-- The full dataset can be found at \url{https://arclab-hku.github.io/ecmd/}. -->
</p>
</div>
</div>
</div>
</li></ol>
</div>
<br>
<div class="Other resources">
<h2>Other resources</h2>
<p style="text-align: justify;">
Some tools for pre-processing the dataset
and HKU event-based handheld & drone dataset are available at <a href="https://github.com/arclab-hku/Event_based_VO-VIO-SLAM" target="_blank" rel="noopener noreferrer">here</a>.
</p>
</div>
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<h2>License</h2>
<p>
This work is released under <a href="https://www.gnu.org/licenses/" target="_blank" rel="noopener noreferrer">GPLv3</a> license.
For commercial inquires, please contact Dr. Peng Lu ([email protected]).
</p>
</div>
<br>
<br>
<!-- Acknowledgements -->
<div class="Acknowledgement">
<h2>Acknowledgement</h2>
<p style="text-align: justify;">
This work was supported by General Research Fund under Grant 17204222, and in part by the Seed Fund for Collaborative Research and General Funding Scheme-HKU-TCL Joint Research Center for Artificial Intelligence.
</p>
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