Commit 6f1535ff authored by cn1n18's avatar cn1n18

Update root.tex

parent 2cf6587e
...@@ -300,7 +300,7 @@ where $y$: depth ground truth, $y_{p}$: a pixel in $y$; $\hat{y}$: depth estimat ...@@ -300,7 +300,7 @@ where $y$: depth ground truth, $y_{p}$: a pixel in $y$; $\hat{y}$: depth estimat
\section{Summary and future work} \section{Conclusions and future work}
In this paper, we have proposed a timestamped, calibrated and synchronized off-road forest depth map dataset recording different obstacles, especially for close-range depth data, such as dirt, tree, tree branch, leaf, and bush. The dataset is recorded under different weather conditions, such as partly sunny, scattered clouds, light rain,sunny and mostly clear. We also evaluate the quality of the depth map by fill rate and the accuracy based on laser emitter, indicates filtering out frames in upward view, and have trained deep neural network by using this forest dataset and shows depth estimation results and its evaluation metrics. We haven't provided a simulation environment, because simulation models do not exist in this domain. Such a very large dataset we provide is particularly useful, there should be some existing methods matching satellite data to ground situations. This dataset should be highly useful in the usage of sparse off-road swarms of ground robots. In this paper, we have proposed a timestamped, calibrated and synchronized off-road forest depth map dataset recording different obstacles, especially for close-range depth data, such as dirt, tree, tree branch, leaf, and bush. The dataset is recorded under different weather conditions, such as partly sunny, scattered clouds, light rain,sunny and mostly clear. We also evaluate the quality of the depth map by fill rate and the accuracy based on laser emitter, indicates filtering out frames in upward view, and have trained deep neural network by using this forest dataset and shows depth estimation results and its evaluation metrics. We haven't provided a simulation environment, because simulation models do not exist in this domain. Such a very large dataset we provide is particularly useful, there should be some existing methods matching satellite data to ground situations. This dataset should be highly useful in the usage of sparse off-road swarms of ground robots.
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