Mesas 042 experiments
Tom Krajnik edited this page Oct 25, 2018
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12 revisions
Datasets and code to replicate experiments of Halodova et al: Adaptive image processing methods for outdoor autonomous vehicles
In this section, we present how to execute scripts to replicate the experiments performed.
- Download datasets to some directory.
- Get the code, i.e. experiments_2018_mesas_exposure branch branch from github.
- Compile the code, source the workspace, run roscore.
- Edit the script process.sh in the directory /stroll_bearnav/mesas_2018_exposure/. If you want to replicate all experiments, make sure that all if statements are true, resp.
1 == 1
. These statements switch on or off the individual experiments described in the paper. - Go to the directory /stroll_bearnav.
- Execute the script process.sh, which is stored in a folder /mesas_2018_exposure. The first argument is the dataset directory. Make sure it is without the last slash. For example
./mesas_2018_exposure/process.sh ~/experiments-mesas-exposure
- Results (also graphs) can be found in the created folder results.
- Make sure that you run the relevant experiments from the previous section, especially that the "Summary map" is created (see the
./mesas_2018_exposure/process.sh
. - Get the code, i.e. experiments_2018_mesas_exposure_predictions branch branch from github.
- Compile the code and source the workspace.
- Edit the
launch/evaluate-predictions.launch
to set a proper folder (thetemporal
folder with the 87 maps). - Go to the directory /stroll_bearnav.
- Execute
./mesas_2018_exposure/build_temporal_statistics.sh stat.txt
to build feature visibility statistics. - Execute
./mesas_2018_exposure/test_temporal_models.sh stat.txt
to build feature visibility statistics. - Copy the resulting
FreMen_X
files to the results folder. - Rerun the
./mesas_2018_exposure/evaluate.sh
script for the statistical tests and graph generation.
- L.Halodova, E.Dvorakova, F.Majer, J.Ulrich, T.Vintr, K.Kusumam, T.Krajnik: Adaptive image processing methods for outdoor autonomous vehicles. In Modelling and Simulation for Autonomous Systems (MESAS), 2018, in review. [bibtex]
This research is supported by the Czech Science Foundation project 17-27006Y STRoLL - Spatio-Temporal Representations for Mobile Robot Navigation.