rewrote green block of data chapter

thesis/Main.tex

@@ -289,6 +289,28 @@ In this chapter, we outline the specific requirements we established for the dat
 
     \newsection{Data}{sec:data}
 
+    
+    %\todo[inline]{describe data sources, limitations}
+    %\todo[inline]{screenshots of camera/3d data?}
+    %\todo[inline]{difficulties: no ground truth, different lidar sensors/settings, different data shapes, available metadata, ...}
+
+    %Our main requirement for the data was for it to be as closely related to the target domain of rescue operations as possible. Since autonomous robots get largely used in situations where a structural failures occured we require of the data to be subterranean. This provides the additional benefit, that data from this domain oftentimes already has some amount of airborne particles like dust due to limited ventilation and oftentimes exposed rock, which is to be expected to also be present in rescue situations. The second and by far more limiting requirement on the data, was that there has to be appreciable degradation due to airborne particles as would occur during a fire from smoke. The type of data has to at least include lidar but for better understanding other types of visual data e.g., visual camera images would be benefical. The amount of data has to be sufficient for training the learning based methods while containing mostly good quality data without degradation, since the semi-supervised method implicitely requires a larger amount of normal than anomalous training for successful training. Nonetheless, the number of anomalous data samples has to be large enough that a comprehensive evaluation of the methods' performance is possible.
+
+    Our primary requirement for the dataset was that it closely reflects the target domain of rescue operations. Because autonomous robots are predominantly deployed in scenarios involving structural failures, the data should be taken from subterranean environments. This setting not only aligns with the operational context but also inherently includes a larger than normal amount of airborne particles (e.g., dust) from limited ventilation and exposed rock surfaces, which is typically encountered during rescue missions.
+
+A second, more challenging requirement is that the dataset must exhibit significant degradation due to airborne particles, as would be expected in scenarios involving smoke from fires. The dataset should at minimum include LiDAR data, and ideally also incorporate other visual modalities (e.g., camera images) to provide a more comprehensive understanding of the environment.
+
+Additionally, the dataset must be sufficiently large for training learning-based methods. Since our semi-supervised approach relies on a predominance of normal data over anomalous data, it is critical that the dataset predominantly consists of high-quality, degradation-free samples. At the same time, there must be enough anomalous samples to allow for a thorough evaluation of the method’s performance.
+
+
+    \todo[inline, color=green!40]{we require lidar sensor data that was collected in a domain as closely related to our target domain (rescue robots indoors, cave-ins, ) as possible which also includes some kind of appreciable degradation for which we have some kind of labeling possibility. ideally the degradation should be from smoke/dust/aerosol particles. most data should be without degradation (since we require more normal than anormal data to train the method as described in X) but we need enough anormal data so we can confidently evaluate the methods performance}
+
+    \todo[inline, color=green!40]{labeling is an especially problematic topic since ideally we would want an analog value which corresponds with the amount of smoke present for evaluation. for training we only require the possibility to provide labels in the form of normal or anormal targets (binary classification) and these labels do not have to be present for all data, only for some of the data (since semi-supervised only uses some labeled data as discussed in X)}
+    \todo[inline, color=green!40]{We chose to evaulate the method on the dataset "Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration"~\cite{alexander_kyuroson_2023_7913307} which is a public dataset collected by X in a sub-terranean environment and includes data from multiple sensors on a moving sensor platform as well as experiments where sensor data is explicitely degraded by aerosol particles produced by a smoke machine.}
+    \todo[inline, color=green!40]{list sensors on the platform}
+    \todo[inline, color=green!40]{talk about how much data is available (maybe a plot about data?), number of experiments with/without degradation, other factors in these experiments which do not concern our use-case of them}
+    \todo[inline, color=green!40]{lidar data of 360° sensor is captured at 10 frames per second. each sensor output consists of pointcloud 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 pointcloud consisting of x,y,z coordinates (relative to sensor platform) as well as some other values per measurement (reflectivity, intensity originally measured range value)}
+
     %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.
 
@@ -315,17 +337,6 @@ While the density of these near-sensor returns might be used to estimate data qu
       \caption{Box diagram depicting the percentage of measurements closer than 50 centimeters to the sensor for normal and anomalous experiments}\label{fig:particles_near_sensor}
     \end{figure}
     %END early returns
-    
-    \todo[inline]{describe data sources, limitations}
-    \todo[inline]{screenshots of camera/3d data?}
-    \todo[inline]{difficulties: no ground truth, different lidar sensors/settings, different data shapes, available metadata, ...}
-    \todo[inline, color=green!40]{we require lidar sensor data that was collected in a domain as closely related to our target domain (rescue robots indoors, cave-ins, ) as possible which also includes some kind of appreciable degradation for which we have some kind of labeling possibility. ideally the degradation should be from smoke/dust/aerosol particles. most data should be without degradation (since we require more normal than anormal data to train the method as described in X) but we need enough anormal data so we can confidently evaluate the methods performance}
-    \todo[inline, color=green!40]{labeling is an especially problematic topic since ideally we would want an analog value which corresponds with the amount of smoke present for evaluation. for training we only require the possibility to provide labels in the form of normal or anormal targets (binary classification) and these labels do not have to be present for all data, only for some of the data (since semi-supervised only uses some labeled data as discussed in X)}
-    \todo[inline, color=green!40]{We chose to evaulate the method on the dataset "Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration"~\cite{alexander_kyuroson_2023_7913307} which is a public dataset collected by X in a sub-terranean environment and includes data from multiple sensors on a moving sensor platform as well as experiments where sensor data is explicitely degraded by aerosol particles produced by a smoke machine.}
-    \todo[inline, color=green!40]{list sensors on the platform}
-    \todo[inline, color=green!40]{talk about how much data is available (maybe a plot about data?), number of experiments with/without degradation, other factors in these experiments which do not concern our use-case of them}
-    \todo[inline, color=green!40]{lidar data of 360° sensor is captured at 10 frames per second. each sensor output consists of pointcloud 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 pointcloud consisting of x,y,z coordinates (relative to sensor platform) as well as some other values per measurement (reflectivity, intensity originally measured range value)}
-
 
     \newsection{Preprocessing Steps}{sec:preprocessing}
     \todo[inline]{describe how 3d lidar data was preprocessed (2d projection), labeling}