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<!DOCTYPE html>
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content="REveL: RGB-Event-LiDAR dataset for assistive robotics">
<meta name="keywords" content="REveL, Assistive Robotics, Event Camera">
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<title>REveL: RGB-Event-LiDAR Dataset for Assistive Robotics</title>
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<h1 class="title is-1 publication-title">REveL Dataset: RGB-Event-LiDAR Dataset for Assistive Robotics</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://www.linkedin.com/in/adam-scicluna-2b1100185/">Adam Scicluna</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com.au/citations?user=SINvQmQAAAAJ&hl=en">Cedric Le Gentil</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://profiles.uts.edu.au/Sheila.Sutjipto">Sheila Sutjipto</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://profiles.uts.edu.au/Gavin.Paul-1">Gavin Paul</a><sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University of Technology Sydney Robotics Institute</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
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<a href="<arxiv link"
class="external-link button is-normal is-rounded is-light">
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<span>Paper</span>
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<a href="http://arxiv.org/abs/2408.13394"
class="external-link button is-normal is-rounded is-dark">
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<span>arXiv</span>
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<span>Data</span>
</a>
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</section>
<!-- Video Only
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<source src="./static/videos/Event-Video-Sample_Conf=0.5_Speed=0.35x.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
Pedestrian tracking and localisation on REveL event camera and LiDAR data utilising RVTs and SORT
</h2>
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</section>
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<!-- Video Column -->
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Your browser does not support the video tag.
</video>
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<div class="media-text">
<h2 class="subtitle">Pedestrian tracking and localisation on REveL event camera and LiDAR data utilising RVTs and SORT</h2>
</div>
</div>
<!-- Image Column -->
<div class="column is-6 has-text-centered">
<div class="media-container">
<img src="./static/images/sensor_suite_combined.jpg"
alt="Sensor suite" class="media-content">
</div>
<div class="media-text">
<h2 class="subtitle">Sensor suite, events (polarity-coloured in red or blue) overlaid on the RGB frame, and LiDAR scan with object motion-captured ground truth poses.</h2>
</div>
</div>
</div>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
The increasing adoption of human-robot interaction presents opportunities for technology to positively impact lives, particularly those with visual impairments, through applications such as guide-dog-like assistive robotics. To fill a gap in the current
landscape of publicly available datasets and provide a means to assist in the development of safer and more robust algorithms
in the future, this website hosts and outlines the dataset introduced in our IEEE CASE 2024 paper named: <b>Towards Robust Perception for Assistive Robotics: An RGB-Event-LiDAR Dataset and Multi-Modal Detection Pipeline.</b>
</p>
<p>
The dataset introduced, named 'REveL', contains RGB, event, point cloud and Inertial Measurement Unit (IMU) data along with ground truth poses of persons in the scene. It is 14.1 minutes in length split over four ROSBags. To complement existing datasets and aid in enhancing detection model generalisation to different scenes, it was collected in an indoor scenario with a handheld sensor suite moving in the field of view of a Vicon motion-capture system. Two people, also tracked by the motion-capture system, are moving in and out of the sensor suite field of view.
</p>
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</div>
</div>
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<h2 class="title is-3">Dataset Information + Structure</h2>
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<p>
To collect the RGB, event, and point cloud data, the sensor suite utilised consisted of:
</p>
<ul>
<li><b>Inivation DAVIS346 event camera:</b> Stream of event data (up to 1MHz) with each event being a tuple of x and y positions in the image space, t the timestamp, and p the polarity of the corresponding illumination change; RGB images at 23Hz; 6-DoF IMU at 1kHz (3-axis gyroscope and 3-axis accelerometer).
</li>
<li><b>Blickfeld Cube1 LiDAR:</b> 3D point clouds at 7.9Hz with point-wise timestamps.
</li>
</ul>
<p>
The sensor’s measurements and output of the motion-capture system were recorded with ROS. We use the rpg dvs ros driver for the DVS camera and the Blickfeld ROS driver for the LiDAR. The sensor suite and helmets worn by persons in the scene were equipped with a set of reflective markers tracked by the Vicon system, subsequently providing the 6-DoF pose of the 2 persons and sensor suite in an arbitrarily fixed reference frame. For convenience and utility, the RGB portion of the dataset is labelled with the class identifier corresponding to the colour helmet worn by each person. In total, the data collected consisted of approximately:
</p>
<ul>
<li>774 million events (~25000 variable length event-array messages)</li>
<li>22000 RGB images</li>
<li>6700 point clouds</li>
<li>70000 ground truth poses each for two persons in the scene</li>
</ul>
<p>
<b>Dataset Structure Here or to the right (could make this section double column - to complete last)</b>
</p>
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<!-- Dataset Information -->
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<!-- Left Column: Dataset Information -->
<div class="column is-three-thirds">
<h2 class="title is-3" style="text-align: center;">Dataset Information</h2>
<div class="content has-text-justified">
<p>
To collect the RGB, event, and point cloud data, the sensor suite utilised consisted of:
</p>
<ul>
<li><b>Inivation DAVIS346 event camera:</b> Stream of event data (up to 1MHz) with each event being a tuple of x and y positions in the image space, t the timestamp, and p the polarity of the corresponding illumination change; RGB images at 23Hz; 6-DoF IMU at 1kHz (3-axis gyroscope and 3-axis accelerometer).
</li>
<li><b>Blickfeld Cube1 LiDAR:</b> 3D point clouds at 7.9Hz with point-wise timestamps.
</li>
</ul>
<p>
The sensor’s measurements and output of the motion-capture system were recorded with ROS. We use the rpg dvs ros driver for the DVS camera and the Blickfeld ROS driver for the LiDAR. The sensor suite and helmets worn by persons in the scene were equipped with a set of reflective markers tracked by the Vicon system, subsequently providing the 6-DoF pose of the 2 persons and sensor suite in an arbitrarily fixed reference frame. For convenience and utility, the RGB portion of the dataset is labelled with the class identifier corresponding to the colour helmet worn by each person. In total, the data collected consisted of approximately:
</p>
<ul>
<li>774 million events (~25000 variable length event-array messages)</li>
<li>22000 RGB images</li>
<li>6700 point clouds</li>
<li>70000 ground truth poses each for two persons in the scene</li>
</ul>
</div>
</div>
<!-- Right Column: Tree Structure -->
<div class="column is-two-fifths">
<h2 class="title is-3" style="text-align: center;">Dataset Structure</h2>
<ul class="tree">
<li>REveL/
<ul>
<li><b>session_1/</b>
<ul>
<li><b>calibration_data/</b>
<ul>
<li>cam_lidar_calib.bag</li>
<li>vicon_cam_calib.bag</li>
<li>INFO.txt</li>
</ul>
</li>
<li><b>dynamic/</b>
<ul>
<li>images.zip
</li>
<li>labels.zip
</li>
<li>classes.txt</li>
<li>notes.json</li>
<li>dynamic.bag</li>
</ul>
</li>
<li>calibration.yaml</li>
</ul>
</li>
<li><b>session_2/</b>
<ul>
<li><b>calibration_data/</b>
<ul>
<li>...</li>
</ul>
</li>
<li><b>dog_like/</b>
<ul>
<li>...</li>
<li>dog_like.bag</li>
</ul>
</li>
<li><b>non_square/</b>
<ul>
<li>...</li>
<li>non_square.bag</li>
</ul>
</li>
<li><b>static/</b>
<ul>
<li>...</li>
<li>static.bag</li>
</ul>
</li>
<li>calibration.yaml</li>
</ul>
</li>
<li>INFO.txt</li>
</ul>
</li>
</ul>
<!--div class="content">
<ul class="directory-tree">
<li>REveL/
<ul>
<li>INFO.txt</li>
<li>session_1/
<ul>
<li>calibration_data/
<ul>
<li>cam_lidar_calib.bag</li>
<li>vicon_cam_calib.bag</li>
<li>INFO.txt</li>
</ul>
</li>
<li>calibration.yaml</li>
<li>dynamic/
<ul>
<li>images/
<ul>
<li><ros-timestamp-1>.jpg</li>
<li><ros-timestamp-2>.jpg</li>
<li>...</li>
</ul>
</li>
<li>labels/
<ul>
<li><ros-timestamp-1>.txt</li>
<li><ros-timestamp-2>.txt</li>
<li>...</li>
</ul>
</li>
<li>classes.txt</li>
<li>notes.json</li>
<li>dynamic.bag</li>
</ul>
</li>
</ul>
</li>
<li>session_2/
<ul>
<li>calibration_data/
<ul>
<li>...</li>
</ul>
</li>
<li>calibration.yaml</li>
<li>dog_like/
<ul>
<li>...</li>
<li>dog_like.bag</li>
</ul>
</li>
<li>non_square/
<ul>
<li>...</li>
<li>non_square.bag</li>
</ul>
</li>
<li>static/
<ul>
<li>...</li>
<li>static.bag</li>
</ul>
</li>
<ul>
</li>
</ul>
</li>
</ul>
</div-->
</div>
</div>
<!--/ Dataset Information -->
</div>
</section>
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<!-- Visual Effects. -->
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<h2 class="title is-3">Visual Effects</h2>
<p>
Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
would be impossible without nerfies since it would require going through a wall.
</p>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/dollyzoom-stacked.mp4"
type="video/mp4">
</video>
</div>
</div>
-->
<!--/ Visual Effects. -->
<!-- Matting. -->
<!--
<div class="column">
<h2 class="title is-3">Matting</h2>
<div class="columns is-centered">
<div class="column content">
<p>
As a byproduct of our method, we can also solve the matting problem by ignoring
samples that fall outside of a bounding box during rendering.
</p>
<video id="matting-video" controls playsinline height="100%">
<source src="./static/videos/matting.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
-->
</div>
<!--/ Matting. -->
<!-- Animation. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Experimentation</h2>
<!-- Interpolating. -->
<!--
<h3 class="title is-4">Interpolating states</h3>
<div class="content has-text-justified">
<p>
We can also animate the scene by interpolating the deformation latent codes of two input
frames. Use the slider here to linearly interpolate between the left frame and the right
frame.
</p>
</div>
<div class="columns is-vcentered interpolation-panel">
<div class="column is-3 has-text-centered">
<img src="./static/images/interpolate_start.jpg"
class="interpolation-image"
alt="Interpolate start reference image."/>
<p>Start Frame</p>
</div>
<div class="column interpolation-video-column">
<div id="interpolation-image-wrapper">
Loading...
</div>
<input class="slider is-fullwidth is-large is-info"
id="interpolation-slider"
step="1" min="0" max="100" value="0" type="range">
</div>
<div class="column is-3 has-text-centered">
<img src="./static/images/interpolate_end.jpg"
class="interpolation-image"
alt="Interpolation end reference image."/>
<p class="is-bold">End Frame</p>
</div>
</div>
<br/>
-->
<!--/ Interpolating. -->
<!-- Comparison. -->
<h3 class="title is-4">Comparison of Event and RGB Detection</h3>
<div class="content has-text-justified">
<p>
We compared the reliability of pedestrian detection in the Event space with detection in the RGB space using YOLOv4.
</p>
</div>
<div class="columns is-vcentered interpolation-panel">
<div class="column is-6 has-text-centered">
<img src="./static/images/1090-Event_-EntireImage-No-Vel-Larger.jpg"
class="interpolation-image"
alt="Interpolation end reference image."/>
<p class="is-bold">Event space</p>
</div>
<div class="column is-6 has-text-centered">
<img src="./static/images/1090-RGB_EntireImage-No-Vel-Larger.jpg"
class="interpolation-image"
alt="Interpolation end reference image."/>
<p class="is-bold">RGB space</p>
</div>
</div>
<br/>
<!--/ Comparison. -->
<!-- Vicon Localisation -->
<h3 class="title is-4">Pose Localisation with Motion Capture</h3>
<div class="content has-text-justified">
<p>
Using the Vicon motion capture system, the poses of two persons in the scene are obtained to be used as a groundtruth for localisation. The below video shows a played back section of 'dynamic.bag' in RViz with the acquired poses overlaid on the LiDAR pointcloud data. Note that when the Vicon system can not accurately track the location of a person, the pose defaults to the origin.
</p>
</div>
<div class="content has-text-centered">
<video id="lidar-pose-video"
controls
muted
preload
playsinline
width="100%">
<source src="./static/videos/Dynamic_LiDAR-Trimmed.mp4"
type="video/mp4">
</video>
</div>
<!--/ Re-rendering. -->
</div>
</div>
<!--/ Animation. -->
<!-- Concurrent Work. -->
<!--
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Related Links</h2>
<div class="content has-text-justified">
<p>
There's a lot of excellent work that was introduced around the same time as ours.
</p>
<p>
<a href="https://arxiv.org/abs/2104.09125">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
</p>
<p>
<a href="https://www.albertpumarola.com/research/D-NeRF/index.html">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/">NR-NeRF</a>
both use deformation fields to model non-rigid scenes.
</p>
<p>
Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/">NSFF</a>, and <a href="https://neural-3d-video.github.io/">DyNeRF</a>
</p>
<p>
There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF">Yen-Chen Lin's curated list of NeRF papers</a>.
</p>
</div>
</div>
</div>
-->
<!--/ Concurrent Work. -->
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@misc{scicluna2024robustperceptionassistiverobotics,
title={Towards Robust Perception for Assistive Robotics: An RGB-Event-LiDAR Dataset and Multi-Modal Detection Pipeline},
author={Adam Scicluna and Cedric Le Gentil and Sheila Sutjipto and Gavin Paul},
year={2024},
eprint={2408.13394},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2408.13394},
}</code></pre>
</div>
</section>
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