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reworked chosen dataset section

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Jan Kowalczyk
2025-05-19 14:31:51 +02:00
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thesis/Main.tex
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@@ -66,6 +66,7 @@
\usepackage{xcolor} \usepackage{xcolor}
\usepackage[colorinlistoftodos]{todonotes} \usepackage[colorinlistoftodos]{todonotes}
\usepackage{makecell}
%\usepackage[disable]{todonotes} %\usepackage[disable]{todonotes}
\DeclareRobustCommand{\threadtodo}[4]{% \DeclareRobustCommand{\threadtodo}[4]{%
@@ -82,6 +83,10 @@
}% }%
} }
\DeclareRobustCommand{\sensorcell}[2]{%
\makecell[l]{#1 \\ \emph{#2}}
}
% correct bad hyphenation % correct bad hyphenation
\hyphenation{} \hyphenation{}
@@ -666,34 +671,105 @@ To mitigate the aforementioned risks we adopt a human-centric, binary labelling
%\todo[inline, color=green!40]{list sensors on the platform} %\todo[inline, color=green!40]{list sensors on the platform}
%Based on the previously discussed requirements and labeling difficulties we decided to train and evaluate the methods on \emph{Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration}~\cite{subter}. The dataset is comprised of data from multiple sensors on a moving sensor platform which was driven through tunnels and rooms in a subterranean setting. What makes it especially fitting for our use case is that during some of the experiments, an artifical smoke machine was employed to simulate aerosol particles. %Based on the previously discussed requirements and labeling difficulties we decided to train and evaluate the methods on \emph{Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration}~\cite{subter}. The dataset is comprised of data from multiple sensors on a moving sensor platform which was driven through tunnels and rooms in a subterranean setting. What makes it especially fitting for our use case is that during some of the experiments, an artifical smoke machine was employed to simulate aerosol particles.
%The sensors employed during capture of the dataset include: %The sensors employed during capture of the dataset include:
Based on the previously discussed requirements and the challenges of obtaining reliable labels, we selected the \emph{Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration}~\cite{subter} for training and evaluation. This dataset comprises multimodal sensor data collected from a moving platform navigating tunnels and rooms in a subterranean environment. Notably, some experiments incorporated an artificial smoke machine to simulate aerosol particles, making the dataset particularly well-suited to our use case. The sensors used during data capture include:\todo[inline, color=green!40]{refer to sketch with numbers} Based on the previously discussed requirements and the challenges of obtaining reliable labels, we selected the \citetitle{subter}~\cite{subter} for training and evaluation. This dataset comprises multimodal sensor data collected from a robotic platform navigating tunnels and rooms in a subterranean environment, an underground tunnel in Luleå, Sweden. Notably, some experiments incorporated an artificial smoke machine to simulate heavy degradation from aerosol particles, making the dataset particularly well-suited to our use case. The sensors used during data capture include:\todo[inline, color=green!40]{refer to sketch with numbers}
% \begin{itemize}
% \item Lidar - Ouster OS1-32
% \item mmWave RADARs - 4 IWR6843AoP ES2.0 based radar models
% \item Lidar - Velodyne Velarray M1600
% \item IR-enabled RBG-D Camera - OAK-D Pro
% \item IMU - Pixhawk 2.1 Cube Orange,
% \end{itemize}
%-------------------------------------------------
% Compact sensor overview (row numbers follow Fig.~\ref{fig:subter_platform})
%-------------------------------------------------
\begin{table}[htbp]
\centering
\caption{On–board sensors recorded in the \citetitle{subter} dataset. Numbers match the labels in Fig.~\ref{fig:subter_platform}; only the most salient details are shown for quick reference.\todo[inline]{check errors}}
\label{tab:sensor-suite-compact}
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\rowcolors{2}{gray!08}{white}
\scriptsize
\begin{tabular}{cp{3.5cm}p{4cm}p{5.5cm}}
\textbf{\#} & \textbf{Sensor} & \textbf{Recorded Data} & \textbf{Key Specs} \\
1 & \sensorcell{Spinning 3-D LiDAR}{Ouster OS1-32} & 3-D cloud, reflectivity & 10 Hz, 32 ch, 360° × 42.4°, $\leq$ 120 m \rule{0pt}{2.6ex} \\
2 & \sensorcell{mm-wave RADAR (×4)}{TI IWR6843AoP} & 4 × 60° RADAR point clouds & 30 Hz, 60 GHz, 9 m max, 0.05 m res. \\
3 & \sensorcell{Solid-state LiDAR}{Velodyne Velarray M1600} & Forward LiDAR cloud & 10 Hz, 160 ch, 120° × 32°, 0.1–30 m \\
4 & \sensorcell{RGB-D / stereo cam}{Luxonis OAK-D Pro} & RGB image, depth map, point cloud & 15 fps, 75 mm baseline, active IR 930 nm \\
5 & \sensorcell{LED flood-light}{RS PRO WL28R} & Scene illumination only & 7 W, 650 lm (no data stream) \\
6 & \sensorcell{IMU}{Pixhawk 2.1 Cube Orange} & Accel, gyro, mag, baro & 190 Hz, 9-DoF, vibration-damped \\
7 & \sensorcell{On-board PC}{Intel NUC i7} & Time-synced logging & Quad-core i7, 16 GB RAM, 500 GB SSD \\
\end{tabular}
\end{table}
%-------------------------------------------------
% Compact sensor overview (row numbers follow Fig.~2b)
%-------------------------------------------------
% \begin{table}[htbp]
% \centering
% \caption{Key on-board sensors deployed during data collection in \citetitle{subter}~\cite{subter}. Sensor numbers correspond to the labels in figure~\ref{fig:subter_platform}.}
% \label{tab:sensor-suite-compact}
% \scriptsize
% \begin{tabular}{@{}clp{3.5cm}p{4.8cm}@{}}
% \# & Sensor (model) & Recorded data type(s) & Core specifications used in the dataset \\
% 1 & Ouster OS1-32 & 3-D spinning-LiDAR cloud + reflectivity & 10 Hz, 32 ch, 360° by 45° FOV, <= 120 m range \\
% 2 & 4 × TI IWR6843AoP & 360° mm-wave RADAR point clouds & 30 Hz, 60 GHz, 9 m max range, 0.05 m range-res. \\
% 3 & Velodyne Velarray M1600 & Solid-state LiDAR cloud & 10 Hz, 160 ch, 120° by 32° FOV, 0.1–30 m range \\
% 4 & Luxonis OAK-D Pro & RGB, stereo depth, point cloud & 15 fps, 75 mm baseline, active IR (930 nm) \\
% 5 & RS PRO WL28R LED & Scene illumination only & 650 lm flood-light (no data stream) \\
% 6 & Pixhawk 2.1 Cube Orange & IMU (accel, gyro, mag, baro) & 190 Hz, 9-DoF, vibration damped \\
% 7 & Intel NUC i7 (ROS host) & Time-synced logging & Quad-core i7, 16 GB RAM, 500 GB SSD \\
% \end{tabular}
% \end{table}
\begin{itemize}
\item Lidar - Ouster OS1-32
\item mmWave RADARs - 4 IWR6843AoP ES2.0 based radar models
\item Lidar - Velodyne Velarray M1600
\item IR-enabled RBG-D Camera - OAK-D Pro
\item IMU - Pixhawk 2.1 Cube Orange,
\end{itemize}
%\todo[inline, color=green!40]{lidar data of 360° sensor is captured at 10 frames per second. each sensor output consists of point cloud which resulted from measurement of 32 vertical channels for each of which 2048 measurement points are taken during each measurement equiangular distributed around the whole horizontal 360°, so the sensor measures 32 * 2048 = 65536 measurements 10 times a second for which ideally every one produces a point in the point cloud consisting of x,y,z coordinates (relative to sensor platform) as well as some other values per measurement (reflectivity, intensity originally measured range value)} %\todo[inline, color=green!40]{lidar data of 360° sensor is captured at 10 frames per second. each sensor output consists of point cloud which resulted from measurement of 32 vertical channels for each of which 2048 measurement points are taken during each measurement equiangular distributed around the whole horizontal 360°, so the sensor measures 32 * 2048 = 65536 measurements 10 times a second for which ideally every one produces a point in the point cloud consisting of x,y,z coordinates (relative to sensor platform) as well as some other values per measurement (reflectivity, intensity originally measured range value)}
%We mainly utilize the data from the \emph{Ouster OS1-32} lidar sensor, which produces 10 frames per second with a resolution of 32 vertical channels by 2048 measurements per channel, both equiangularly spaced over the vertical and horizontal fields of view of 42.4° and 360° respectively. Every measurement of the lidar therefore results in a point cloud with a maximum of 65536 points. Every point contains the \emph{X}, \emph{Y} and \emph{Z} coordinates in meters with the sensor location as origin, as well as values for the \emph{range}, \emph{intensity} and \emph{reflectivity} which are typical data measured by lidar sensors. The data is dense, meaning missing measurements are still present in the data of each point cloud with zero values for most fields. %We mainly utilize the data from the \emph{Ouster OS1-32} lidar sensor, which produces 10 frames per second with a resolution of 32 vertical channels by 2048 measurements per channel, both equiangularly spaced over the vertical and horizontal fields of view of 42.4° and 360° respectively. Every measurement of the lidar therefore results in a point cloud with a maximum of 65536 points. Every point contains the \emph{X}, \emph{Y} and \emph{Z} coordinates in meters with the sensor location as origin, as well as values for the \emph{range}, \emph{intensity} and \emph{reflectivity} which are typical data measured by lidar sensors. The data is dense, meaning missing measurements are still present in the data of each point cloud with zero values for most fields.
\todo[inline, color=green!40]{short description of sensor platform and refer to photo} \todo[inline, color=green!40]{short description of sensor platform and refer to photo}
We use data from the \emph{Ouster OS1-32} LiDAR sensor, which was configured to capture 10 frames per second with a resolution of 32 vertical channels and 2048 measurements per channel. These settings yield equiangular measurements across a vertical field of view of 42.4° and a complete 360° horizontal field of view. Consequently, every LiDAR scan can generate up to 65,536 points. Each point contains the \emph{X}, \emph{Y}, and \emph{Z} coordinates (in meters, with the sensor location as the origin) along with values for \emph{range}, \emph{intensity}, and \emph{reflectivity}—typical metrics measured by LiDAR sensors. Although the dataset is considered dense, each point cloud still contains missing measurements, with fields of these missing measurements registering as zero. We use data from the \emph{Ouster OS1-32} LiDAR sensor, which was configured to capture 10 frames per second with a resolution of 32 vertical channels and 2048 measurements per channel. These settings yield equiangular measurements across a vertical field of view of 42.4° and a complete 360° horizontal field of view. Consequently, every LiDAR scan can generate up to 65,536 points. Each point contains the \emph{X}, \emph{Y}, and \emph{Z} coordinates (in meters, with the sensor location as the origin) along with values for \emph{range}, \emph{intensity}, and \emph{reflectivity}—typical metrics measured by LiDAR sensors. The datasets' point clouds are saved in a dense format, meaning each of the 65,536 measurements is present in the data, although fields for missing measurements contain zeroes.
\begin{figure} % \begin{figure}
% \centering
% \subfigure{\includegraphics[width=0.45\textwidth]{figures/data_subter_platform_photo.jpg}\label{fig:subter_platform_sketch}}%
% \hfill
% \subfigure{\includegraphics[width=0.45\textwidth]{figures/data_subter_platform_sketch.png}\label{fig:subter_platform_photo}}%
% \caption{\todo[inline, color=green!40]{better caption} 1-OS1-32, 2-mmWave RADARs, 3-M1600, 4-OAK-D Pro. 5-LED, 6-IMU, and 7-Intel NUC. Reproduced from~\cite{subter}}\label{fig:subter_platform}
% \end{figure}
%-------------------------------------------------
% Platform photographs – (a) Pioneer base, (b) numbered sensor layout
%-------------------------------------------------
\begin{figure}[htbp]
\centering \centering
\subfigure{\includegraphics[width=0.45\textwidth]{figures/data_subter_platform_photo.jpg}\label{fig:subter_platform_sketch}}% \subfigure[Pioneer 3-AT2 mobile base carrying the sensor tower.
The four-wheel, skid-steered platform supports up to 30 kg
payload and can negotiate rough terrain—providing the
mobility required for subterranean data collection.]
{\includegraphics[width=0.45\textwidth]{figures/data_subter_platform_photo.jpg}
\label{fig:subter_platform_photo}}
\hfill \hfill
\subfigure{\includegraphics[width=0.45\textwidth]{figures/data_subter_platform_sketch.png}\label{fig:subter_platform_photo}}% \subfigure[Sensor layout and numbering.
\caption{\todo[inline, color=green!40]{better caption} 1-OS1-32, 2-mmWave RADARs, 3-M1600, 4-OAK-D Pro. 5-LED, 6-IMU, and 7-Intel NUC. Reproduced from~\cite{subter}}\label{fig:subter_platform} Components: 1 OS1-32 LiDAR, 2 mm-wave RADARs, 3 M1600 LiDAR,
4 OAK-D Pro camera, 5 LED flood-light, 6 IMU, 7 Intel NUC.
See Table~\ref{tab:sensor-suite-compact} for detailed
specifications.]
{\includegraphics[width=0.45\textwidth]{figures/data_subter_platform_sketch.png}
\label{fig:subter_platform_sketch}}
\caption{Robotic platform and sensor configuration used to record the dataset.}
\label{fig:subter_platform}
\end{figure} \end{figure}
%During the measurement campaign 14 experiments were conducted, of which 10 did not contain the utilization of the artifical smoke machine and 4 which did contain the artifical degradation, henceforth refered to as normal and anomalous experiments respectively. During 13 of the experiments the sensor platform was in near constant movement (sometimes translation - sometimes rotation) with only 1 anomalous experiment having the sensor platform stationary. This means we do not have 2 stationary experiments to directly compare the data from a normal and an anomalous experiment, where the sensor platform was not moved, nonetheless the genereal experiments are similar enough for direct comparisons. During anomalous experiments the artifical smoke machine appears to have been running for some time before data collection, since in camera images and lidar data alike, the water vapor appears to be distributed quite evenly throughout the closer perimeter of the smoke machine. The stationary experiment is also unique in that the smoke machine is quite close to the sensor platform and actively produces new smoke, which is dense enough for the lidar data to see the surface of the newly produced water vapor as a solid object. %During the measurement campaign 14 experiments were conducted, of which 10 did not contain the utilization of the artifical smoke machine and 4 which did contain the artifical degradation, henceforth refered to as normal and anomalous experiments respectively. During 13 of the experiments the sensor platform was in near constant movement (sometimes translation - sometimes rotation) with only 1 anomalous experiment having the sensor platform stationary. This means we do not have 2 stationary experiments to directly compare the data from a normal and an anomalous experiment, where the sensor platform was not moved, nonetheless the genereal experiments are similar enough for direct comparisons. During anomalous experiments the artifical smoke machine appears to have been running for some time before data collection, since in camera images and lidar data alike, the water vapor appears to be distributed quite evenly throughout the closer perimeter of the smoke machine. The stationary experiment is also unique in that the smoke machine is quite close to the sensor platform and actively produces new smoke, which is dense enough for the lidar data to see the surface of the newly produced water vapor as a solid object.
During the measurement campaign, 14 experiments were conducted—10 without the artificial smoke machine (hereafter referred to as normal experiments) and 4 with it (anomalous experiments). In 13 of these experiments, the sensor platform was in near-constant motion (either translating or rotating), with only one anomalous experiment conducted while the platform remained stationary. Although this means we do not have two stationary experiments from the same exact position for a direct comparison between normal and anomalous conditions, the overall experiments are similar enough to allow for meaningful comparisons. During the measurement campaign, a total of 14 experiments were conducted—10 prior to operating the artificial smoke machine (hereafter referred to as normal experiments) and 4 after it has already been running for some time (anomalous experiments). In 13 of these experiments, the sensor platform was in near-constant motion (either translating at roughly 1m/s or rotating), with only one anomalous experiment conducted while the platform remained stationary. Although this means we do not have two stationary experiments from the same exact position for a direct comparison between normal and anomalous conditions, the overall experiments are similar enough to allow for meaningful comparisons. In addition to the presence of water vapor from the smoke machine, the experiments vary in illumination conditions, the presence of humans on the measurement grounds, and additional static artifacts. For our purposes, only the artificial smoke is relevant; differences in lighting or incidental static objects do not affect our analysis. Regardless of illumination, the LiDAR sensor consistently produces comparable point clouds, and the presence of static objects does not influence our quantification of point cloud degradation.
In the anomalous experiments, the artificial smoke machine appears to have been running for some time before data collection began, as evidenced by both camera images and LiDAR data showing an even distribution of water vapor around the machine. The stationary experiment is particularly unique: the smoke machine was positioned very close to the sensor platform and was actively generating new, dense smoke, to the extent that the LiDAR registered the surface of the fresh water vapor as if it were a solid object. In the anomalous experiments, the artificial smoke machine appears to have been running for some time before data collection began, as evidenced by both camera images and LiDAR data showing an even distribution of water vapor around the machine. The stationary experiment is particularly unique: the smoke machine was positioned very close to the sensor platform and was actively generating new, dense smoke, to the extent that the LiDAR registered the surface of the fresh water vapor as if it were a solid object.
@@ -701,8 +777,6 @@ In the anomalous experiments, the artificial smoke machine appears to have been
%The 14 experiments differ regarding the available illumination, the presence of humans-traversing the measurement grounds- or additional static objects as artifcats and of course regarding the presence of the water vapor from the smoke machine. Aside from the artifical smoke which is essential for our use case, the other differences during the individual experiments are of no interestet to us and do not affect it in any way. Regardless of illumination, the lidar sensor produces indistinguishable point clouds and any static objects do not factor into our quantification of the point clouds' degradation. %The 14 experiments differ regarding the available illumination, the presence of humans-traversing the measurement grounds- or additional static objects as artifcats and of course regarding the presence of the water vapor from the smoke machine. Aside from the artifical smoke which is essential for our use case, the other differences during the individual experiments are of no interestet to us and do not affect it in any way. Regardless of illumination, the lidar sensor produces indistinguishable point clouds and any static objects do not factor into our quantification of the point clouds' degradation.
The 14 experiments varied in illumination conditions, the presence of humans on the measurement grounds, and additional static artifacts, as well as in the presence of water vapor from the smoke machine. For our purposes, only the artificial smoke is relevant; differences in lighting or incidental static objects do not affect our analysis. Regardless of illumination, the LiDAR sensor consistently produces comparable point clouds, and the presence of static objects does not influence our quantification of point cloud degradation.
%\todo[inline, color=green!40]{include representative image of point cloud and camera image} %\todo[inline, color=green!40]{include representative image of point cloud and camera image}
The figures~\ref{fig:data_screenshot_pointcloud}~and~\ref{fig:data_screenshot_camera} show an representative depiction of the environment of the experiments as a camera image of the IR camera and the point cloud created by the OS1 lidar sensor at practically the same time. The figures~\ref{fig:data_screenshot_pointcloud}~and~\ref{fig:data_screenshot_camera} show an representative depiction of the environment of the experiments as a camera image of the IR camera and the point cloud created by the OS1 lidar sensor at practically the same time.
@@ -718,20 +792,27 @@ Regarding the dataset volume, the 10 normal experiments ranged from 88.7 to 363.
\fig{data_points_pie}{figures/data_points_pie.png}{Pie chart visualizing the amount and distribution of normal and anomalous point clouds in \cite{subter}} \fig{data_points_pie}{figures/data_points_pie.png}{Pie chart visualizing the amount and distribution of normal and anomalous point clouds in \cite{subter}}
%BEGIN missing points %BEGIN missing points
As we can see in figure~\ref{fig:data_missing_points}, the artifical smoke introduced as explicit degradation during some experiments results in more missing measurements during scans, which can be explained by measurement rays hitting airborne particles but not being reflected back to the sensor in a way it can measure. %\todo[inline]{merge next two paragraphs into: \emph{how the degradation affects the poinntclouds - statistics. as discussed this may not be all but it gives an overview about the introduced degradation }}
%As we can see in figure~\ref{fig:data_missing_points}, the artifical smoke introduced as explicit degradation during some experiments results in more missing measurements during scans, which can be explained by measurement rays hitting airborne particles but not being reflected back to the sensor in a way it can measure.
%END missing points
The artificial smoke introduces measurable changes that clearly separate the \textit{anomalous} runs from the \textit{normal} baseline. One change is a larger share of missing points per scan: smoke particles scatter or absorb the laser beam before it reaches a solid target, so the sensor reports an error instead of a distance. Figure~\ref{fig:data_missing_points} shows the resulting right–shift of the missing-point histogram, a known effect for lidar sensors in aerosol-filled environments~\cite{when_the_dust_settles}. Another demonstrative effect is the appearance of many spurious returns very close to the sensor; these near-field points arise when back-scatter from the aerosol itself is mistaken for a surface echo. The box-plot in Fig.~\ref{fig:particles_near_sensor} confirms a pronounced increase in sub-50 cm hits under smoke, consistent with the behaviour reported in \citetitle{when_the_dust_settles}~\cite{when_the_dust_settles}.
\fig{data_missing_points}{figures/data_missing_points.png}{Density histogram showing the percentage of missing measurements per scan for normal experiments without degradation and anomalous experiments with artifical smoke introduced as degradation.} \fig{data_missing_points}{figures/data_missing_points.png}{Density histogram showing the percentage of missing measurements per scan for normal experiments without degradation and anomalous experiments with artifical smoke introduced as degradation.}
%END missing points
\fig{particles_near_sensor}{figures/particles_near_sensor_boxplot_zoomed_500.png}{Box diagram depicting the percentage of measurements closer than 50 centimeters to the sensor for normal and anomalous experiments}
Taken together, the percentage of missing points and the proportion of near-sensor returns provide a concise indication of how strongly the smoke degrades our scans—capturing the two most prominent aerosol effects, drop-outs and back-scatter spikes. They do not, however, reveal the full error landscape discussed earlier (compound errors, temperature drift, multipath, \dots), so they should be read as an easily computed synopsis rather than an exhaustive measure of LiDAR quality. Next we will discuss how the lidar scans were preprocessed before use and how we actually assigned ground-truth labels to each scan, so we could train and evaluate our quantification degradation methods.
%BEGIN early returns %BEGIN early returns
% In experiments with artifical smoke present, we observe many points in the point cloud very close to the sensor where there are no solid objects and therefore the points have to be produced by airborne particles from the artifical smoke. The phenomenon can be explained, in that the closer to the sensor an airborne particle is hit, the higher the chance of it reflecting the ray in a way the lidar can measure. In \ref{fig:particles_near_sensor} we see a box diagram depicting how significantly more measurements of the anomaly expirements produce a range smaller than 50 centimeters. Due to the sensor platform's setup and its paths taken during experiments we can conclude that any measurement with a range smaller than 50 centimeters has to be erroneous. While the amount of these returns near the sensor could most likely be used to estimate the sensor data quality while the sensor itself is located inside an environment containing airborne particles, this method would not allow to anticipate sensor data degradation before the sensor itself enters the affected area. Since lidar is used to sense the visible geometry from a distance, it would be desireable to quantify the data degradation of an area before the sensor itself enters it. Due to these reasons we did not use this phenomenon in our work. % In experiments with artifical smoke present, we observe many points in the point cloud very close to the sensor where there are no solid objects and therefore the points have to be produced by airborne particles from the artifical smoke. The phenomenon can be explained, in that the closer to the sensor an airborne particle is hit, the higher the chance of it reflecting the ray in a way the lidar can measure. In \ref{fig:particles_near_sensor} we see a box diagram depicting how significantly more measurements of the anomaly expirements produce a range smaller than 50 centimeters. Due to the sensor platform's setup and its paths taken during experiments we can conclude that any measurement with a range smaller than 50 centimeters has to be erroneous. While the amount of these returns near the sensor could most likely be used to estimate the sensor data quality while the sensor itself is located inside an environment containing airborne particles, this method would not allow to anticipate sensor data degradation before the sensor itself enters the affected area. Since lidar is used to sense the visible geometry from a distance, it would be desireable to quantify the data degradation of an area before the sensor itself enters it. Due to these reasons we did not use this phenomenon in our work.
In experiments with artificial smoke, we observe numerous points in the point cloud very close to the sensor, even though no solid objects exist at that range. These points are therefore generated by airborne particles in the artificial smoke. This phenomenon likely occurs because the closer an airborne particle is to the sensor, the higher the probability it reflects the laser beam in a measurable way. As shown in Figure~\ref{fig:particles_near_sensor}, a box diagram illustrates that significantly more measurements during these experiments report ranges shorter than 50 centimeters. Given the sensor platform's setup and its experimental trajectory, we conclude that any measurement with a range under 50 centimeters is erroneous. %In experiments with artificial smoke, we observe numerous points in the point cloud very close to the sensor, even though no solid objects exist at that range, which is consistent with lidar's expected behavior for these circumstances~\cite{when_the_dust_settles}. These points are generated by airborne particles in the artificial smoke, for which hold that closer airborne particles to the sensor have a higher probability of reflecting the laser beam in a measurable way. As shown in Figure~\ref{fig:particles_near_sensor}, a box diagram illustrates that significantly more measurements during these experiments report ranges shorter than 50 centimeters. Given the sensor platform's setup and its experimental trajectory, we conclude that any measurement with a range under 50 centimeters is erroneous.
While the density of these near-sensor returns might be used to estimate data quality when the sensor is already in an environment with airborne particles, this method cannot anticipate data degradation before the sensor enters such an area. Since LiDAR is intended to capture visible geometry from a distance, it is preferable to quantify potential degradation of an area in advance. For these reasons, we did not incorporate this phenomenon into our subsequent analysis. %While the density of these near-sensor returns might be used to estimate data quality when the sensor is already in an environment with airborne particles, this method cannot anticipate data degradation before the sensor enters such an area. Since LiDAR is intended to capture visible geometry from a distance, it is preferable to quantify potential degradation of an area in advance. For these reasons, we did not incorporate this phenomenon into our subsequent analysis.
\fig{particles_near_sensor}{figures/particles_near_sensor_boxplot_zoomed_500.png}{Box diagram depicting the percentage of measurements closer than 50 centimeters to the sensor for normal and anomalous experiments}
%END early returns %END early returns
\newsection{preprocessing}{Preprocessing Steps and Labeling} \newsection{preprocessing}{Preprocessing Steps and Labeling}

15
thesis/bib/bibliography.bib
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@@ -514,6 +514,21 @@
Williams, Jason L. and Carlone, Luca}, Williams, Jason L. and Carlone, Luca},
year = {2024}, year = {2024},
pages = {936–959}, pages = {936–959},
},
@article{when_the_dust_settles,
title = {When the Dust Settles: The Four Behaviors of LiDAR in the Presence of
Fine Airborne Particulates},
volume = {34},
ISSN = {1556-4967},
url = {http://dx.doi.org/10.1002/rob.21701},
DOI = {10.1002/rob.21701},
number = {5},
journal = {Journal of Field Robotics},
publisher = {Wiley},
author = {Phillips, Tyson Govan and Guenther, Nicky and McAree, Peter Ross},
year = {2017},
month = feb,
pages = {985–1009},
} }