52 lines
1.5 KiB
Markdown
52 lines
1.5 KiB
Markdown
## SVO Analysis
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#### Download Test Data
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Create a folder where you will store all datasets, e.g.:
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mkdir Datasets
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In your `.bashrc` script, create a new environment variable pointing to the dataset folder:
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export SVO_DATASET_DIR=${HOME}/Datasets
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Source your new `.bashrc` script, go to the new dataset folder and download the test data:
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source `~/.bashrc`
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cd ${SVO_DATASET_DIR}
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wget http://rpg.ifi.uzh.ch/datasets/flying_room_1_rig_1.tar.gz -O - | tar -xz
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#### Run Benchmark
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In the _CMakeLists.txt_ of SVO you need to set the `TRACE` flag to `TRUE` and recompile SVO.
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Afterwards run
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rosrun svo_analysis benchmark.py flying_room_1_fast
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The script will create a new folder in `svo_analysis/results/`, run svo on the dataset and save the tracefile in this folder and at the end it will run some scripts to generate plots from the tracefile.
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#### Dataset file
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A dataset with motion-capture-system ground-truth should contain the following files
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* _images.txt_
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# timestamp img/imagename
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1329330952.725747585296631 img/00000.png
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1329330952.765715122222900 img/00001.png
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1329330952.805604219436646 img/00002.png
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...
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* _groundtruth.txt_: groundtruth poses as acquired from a motion capture system
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# timestamp tx ty tz qx qy qz qw
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* _groundtruth_matched.txt_: the groundtruth pose associated to the images in images.txt. This file can be generated by the command:
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cd my_dataset
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rosrun svo_analysis associate.py images.txt groundtruth.txt
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