wip

thesis/Main.tex

@@ -284,6 +284,9 @@
 
     \todo[inline, color=green!40]{while as described in sec X the method DeepSAD is not dependend on any specific type/structure of data it requires to train an auto encoder in the pretraining step. such autoencoders are better understood in the image domain since there are many uses cases for this such as X (TODO citation needed), there are also 3d data auto encoders such as X (todo find example). same as the reference paper (rain cite) we chose to transform the 3d data to 2d by using a spherical spherical projection to map each of the 3d points onto a 2d plane where the range of each measurement can be expressed as the brightness of a single pixel. this leaves us with a 2d image of resolution 32x2048 (channels by horizontal measurements), which is helpful for visualization as well as for choosing a simpler architecture for the autoencoder of deepsad, the data in the rosbag is sparse meaning that measurements of the lidar which did not produce any value (no return ray detected before sensor specific timeout) are simply not present in the lidar scan. meaning we have at most 65xxx measurements per scan but mostly fewer than this, (maybe statistic about this? could aslo be interesting to show smoke experiment stuff)}
     \todo[inline, color=green!40]{to achieve this transformation we used the helpful measurement index and channel present in each measurement point of the dataset which allowed a perfect reconstruction of the 2d projection without calculating the pixel position in the projection of each measurement via angles which in our experience typically leads to some ambiguity in the projection (multiple measurements mapping to the same pixel due to precision loss/other errors) the measurement index increases even for unavailable measurements (no ray return) so we can simply create the 2d projection by mapping the normalized range (FIXME really normalized) value to the pixel position y = channel, x = measurement index. by initalizing the array to NaN values originally we have a 2d data structure with the range values and NaN on pixel positions where originally no measurement took place (missing measurements in scans due to no ray return)}
+    \todo[inline, color=green!40]{another important preprocessing step is labeling of the lidar frames as normal/anormal. this is one hand used during training (experiments with zero labeled up to most of the data being labeled) and on the other hand is important for evaluation of the method performance. originally we do not have any labels on the data regarding degradation and no analog values from another sensor which measures current smoke particles in the air. our simple approach was to label all frames from experiments which included artifical degradation by fog machine smoke as anomalous and all frames from experiments without artifical degradation as normal.}
+    \todo[inline, color=green!40]{this simple labeling method is quite flawed since we do not label based on the actual degradation of the scan (not by some kind of threshold of analog measurement threshold, statistical info about scan) since (TODO FIXME) this would result in training which only learns this given metric (example missing measurement points) which would make this methodology useless since we could simply use that same measurement as an more simple way to quantify the scan's degradation. }
+    \todo[inline]{TODO maybe evaluate based on different thresholds? missing datapoints, number of detected outliers, number of particles in phantom circle around sensor?}
 
 
 \newchapter{Experimental Setup}{chap:experimental_setup}