## SVO Analysis

#### Download Test Data

Create a folder where you will store all datasets, e.g.:

    mkdir Datasets

In your `.bashrc` script, create a new environment variable pointing to the dataset folder:

    export SVO_DATASET_DIR=${HOME}/Datasets

Source your new `.bashrc` script, go to the new dataset folder and download the test data:

    source `~/.bashrc`
    cd ${SVO_DATASET_DIR}
    wget http://rpg.ifi.uzh.ch/datasets/flying_room_1_rig_1.tar.gz -O - | tar -xz

TODO: replace this dataset with new ones.

#### Run Benchmark

In the _CMakeLists.txt_ of SVO you need to set the `TRACE` flag to `TRUE` and recompile SVO.
Afterwards run

    rosrun svo_analysis benchmark.py flying_room_1_fast

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.


#### Dataset file

A dataset with motion-capture-system ground-truth should contain the following files

* _images.txt_
  
     # timestamp img/imagename
     1329330952.725747585296631 img/00000.png
     1329330952.765715122222900 img/00001.png
     1329330952.805604219436646 img/00002.png
     ...

* _groundtruth.txt_: groundtruth poses as acquired from a motion capture system

     # timestamp tx ty tz qx qy qz qw
     
* _groundtruth_matched.txt_: the groundtruth pose associated to the images in images.txt. This file can be generated by the command:

    cd my_dataset
    rosrun svo_analysis associate.py images.txt groundtruth.txt