grammarly intro

thesis/Main.tex

@@ -205,11 +205,11 @@ Autonomous robots are increasingly used in search and rescue (SAR) missions, as
 
 Search and rescue environments pose difficult conditions for sensor systems to produce reliable data. A prominent challenge is the presence of aerosol particles such as smoke and dust, which can obstruct visibility and cause sensors to generate erroneous data. If such degraded conditions were not represented in the training data of the robot’s algorithms, these errors may lead to unexpected outputs and potentially endanger both the robot and human rescue targets. This is especially critical for autonomous robots, whose decisions rely entirely on sensor data without human oversight. To mitigate these risks, robots must be able to assess the trustworthiness of their sensor data.
 
-For remote controlled robots a human operator can make these decisions but many search and rescue missions do not allow remote control due to environmental factors, such as radio signal attenuation or the search area's size and therefore demand autonomous robots. Therefore, during the design for such robots we arrive at the following critical \rev{research} question:
+For remotely controlled robots, a human operator can make these decisions, but many search and rescue missions do not allow remote control due to environmental factors, such as radio signal attenuation or the search area's size, and therefore demand autonomous robots. Therefore, during the design of such robots, we arrive at the following critical \rev{research} question:
 
 \begin{quote} Can autonomous robots quantify the reliability of \rev{LiDAR} sensor data in hazardous environments to make more informed decisions? \end{quote}
 
-In this thesis we aim to answer this question by assessing a deep learning-based anomaly detection method and its performance when quantifying the sensor data's degradation. The employed algorithm is a semi-supervised anomaly detection algorithm which uses manually labeled training data to improve its performance over unsupervised methods. We compare the method's performance with common baseline methods from the same class of algorithms. The model's output is an anomaly score which quantifies the data reliability and can be used by algorithms that rely on the sensor data. These reliant algorithms may decide to for example slow down the robot to collect more data, choose alternative routes, signal for help or rely more heavily on other sensor's input data.
+In this thesis, we aim to answer this question by assessing a deep learning-based anomaly detection method and its performance when quantifying the sensor data's degradation. The employed algorithm is a semi-supervised anomaly detection algorithm that uses manually labeled training data to improve its performance over unsupervised methods. We compare the method's performance with common baseline methods from the same class of algorithms. The model's output is an anomaly score that quantifies the data reliability and can be used by algorithms that rely on the sensor data. These reliant algorithms may decide to, for example, slow down the robot to collect more data, choose alternative routes, signal for help, or rely more heavily on other sensors' input data.
 
 Our experiments demonstrate that anomaly detection methods are indeed applicable to this task, allowing \rev{LiDAR} data degradation to be quantified on subterranean datasets representative of SAR environments. Among the tested approaches, the semi-supervised method consistently outperformed established baselines. At the same time, the lack of suitable training data—and in particular the scarcity of reliable evaluation labels—proved to be a major limitation, constraining the extent to which the expected real-world performance of these methods could be assessed.
 
@@ -220,7 +220,7 @@ In this thesis, we focus our research on the unique challenges faced by autonomo
 
 While robotic computer vision systems often incorporate a variety of sensors—such as time-of-flight cameras, infrared cameras, and ultrasound sensors—we found that autonomous rescue robots primarily depend on \rev{LiDAR} data for mapping and navigation. \rev{LiDAR} sensors offer high accuracy, high resolution, and an extensive field of view (often a full 360° \rev{horizontal} and a substantial vertical coverage), which are essential for constructing comprehensive environmental maps in challenging scenarios. Furthermore, the cost of \rev{LiDAR} sensors has decreased significantly in recent decades, driven by their widespread adoption in autonomous driving, drones, and robotics, as well as manufacturing advancements like microelectromechanical systems (MEMS). For these reasons, our research is focused exclusively on \rev{LiDAR} sensor data—specifically, the point clouds generated within a defined coordinate system. Although sensor fusion techniques are commonly used to enhance data accuracy and confidence, incorporating fused data would not only add significant complexity to our study but also limit our analysis to platforms equipped with all the sensor types involved. Consequently, we concentrate on quantifying sensor degradation solely through \rev{LiDAR} data.
 
-The method we employ produces an analog score that reflects the confidence in the sensor data, with lower confidence indicating higher degradation. Although we do not investigate the direct applications of this score, potential uses include simple thresholding to decide whether to proceed with a given action as well as dynamically adjusting the robot's speed based on data quality to collect additional data when confidence is low. Importantly, this output score is a snapshot for each \rev{LiDAR} scan and does not incorporate temporal information. While many \rev{LiDAR} sensors capture multiple scans per second—enabling the possibility of time-series analyses such as running averages or more advanced statistical evaluations—we focus solely on individual scans without examining the differences between successive scans.
+The method we employ produces an analog score that reflects the confidence in the sensor data, with lower confidence indicating higher degradation. Although we do not investigate the direct applications of this score, potential uses include simple thresholding to decide whether to proceed with a given action, as well as dynamically adjusting the robot's speed based on data quality to collect additional data when confidence is low. Importantly, this output score is a snapshot for each \rev{LiDAR} scan and does not incorporate temporal information. While many \rev{LiDAR} sensors capture multiple scans per second—enabling the possibility of time-series analyses such as running averages or more advanced statistical evaluations—we focus solely on individual scans without examining the differences between successive scans.
 
 \newsection{thesis_structure}{Structure of the Thesis}
 

thesis/thesis_preamble/abstract.tex

@@ -1,9 +1,9 @@
 \addcontentsline{toc}{chapter}{Abstract}
 \begin{center}\Large\bfseries Abstract\end{center}\vspace*{1cm}\noindent
-Autonomous robots are increasingly used in search and rescue (SAR) missions. In these missions, lidar sensors are often the most important source of environmental data. However, lidar data can degrade under hazardous conditions, especially when airborne particles such as smoke or dust are present. This degradation can lead to errors in mapping and navigation and may endanger both the robot and humans. Robots therefore need a way to estimate the reliability of their lidar data, so \rev{that} they can make better informed decisions.
+Autonomous robots are increasingly used in search and rescue (SAR) missions. In these missions, lidar sensors are often the most important source of environmental data. However, lidar data can degrade under hazardous conditions, especially when airborne particles such as smoke or dust are present. This degradation can lead to errors in mapping and navigation and may endanger both the robot and humans. Therefore, robots need a way to estimate the reliability of their lidar data, so \rev{that} they can make better-informed decisions.
 \bigskip
 
-This thesis investigates whether anomaly detection methods can be used to quantify lidar data degradation \rev{caused by airborne particles such as smoke and dust}. We apply a semi-supervised deep learning approach called DeepSAD which produces an anomaly score for each lidar scan, serving as a measure of data reliability.
+This thesis investigates whether anomaly detection methods can be used to quantify lidar data degradation \rev{caused by airborne particles such as smoke and dust}. We apply a semi-supervised deep learning approach called DeepSAD, which produces an anomaly score for each lidar scan, serving as a measure of data reliability.
 \bigskip
 
-We evaluate this method against baseline methods on an subterranean dataset that includes lidar scans degraded by artificial smoke. Our results show that DeepSAD consistently outperforms the baselines and can clearly distinguish degraded from normal scans. At the same time, we find that the limited availability of labeled data and the lack of robust ground truth remain major challenges. Despite these limitations, our work demonstrates that anomaly detection methods are a promising tool for lidar degradation quantification in SAR scenarios.
+We evaluate this method against baseline methods on a subterranean dataset that includes lidar scans degraded by artificial smoke. Our results show that DeepSAD consistently outperforms the baselines and can clearly distinguish degraded from normal scans. At the same time, we find that the limited availability of labeled data and the lack of robust ground truth remain major challenges. Despite these limitations, our work demonstrates that anomaly detection methods are a promising tool for lidar degradation quantification in SAR scenarios.

thesis/thesis_preamble/acknowledgements.tex

@@ -1,3 +1,3 @@
-\addcontentsline{toc}{chapter}{Artifical Intelligence Usage Disclaimer}
+\addcontentsline{toc}{chapter}{Artificial Intelligence Usage Disclaimer}
-\begin{center}\Large\bfseries Artifical Intelligence Usage Disclaimer\end{center}\vspace*{1cm}\noindent
+\begin{center}\Large\bfseries Artificial Intelligence Usage Disclaimer\end{center}\vspace*{1cm}\noindent
-During the creation of this thesis an LLM-based Artifical Intelligence tool was used for stylistic and grammatical revision of the author's own work.
+During the creation of this thesis, an LLM-based Artificial Intelligence tool was used for stylistic and grammatical revision of the author's own work.