We demonstrate the usage of the system with the `MH_05_difficult` sequence of the [EuRoC dataset](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) as an example.
**Note:** The path to calibration and configuration files used here works for the APT installation. If you compile from source specify the appropriate path to the files in [data folder](/data/).
This opens the GUI and runs the sequence. The processing happens in the background as fast as possible, and the visualization results are saved in the GUI and can be analysed offline.
By default the system starts with `continue_fast` enabled. This option visualizes the latest processed frame until the end of the sequence. Alternatively, the `continue` visualizes every frame without skipping. If both options are disabled the system shows the frame that is selected with the `show_frame` slider and the user can move forward and backward with `next_step` and `prev_step` buttons. The `follow` button changes between the static camera and the camera attached to the current frame.
For evaluation the button `align_svd` is used. It aligns the GT trajectory with the current estimate using an SE(3) transformation and prints the transformation and the root-mean-squared absolute trajectory error (RMS ATE).
The buttons in the GUI have the following meaning:
*`show_frame1`, `show_cam1`, `show_frame2`, `show_cam2` allows you to assign images to image view 1 and 2 from different timestamps and cameras.
*`show_detected` shows the detected keypoints in the image view window.
*`show_matches` shows feature matching results.
*`show_inliers` shows inlier matches after geometric verification.
*`show_ids` prints point IDs. Can be used to find the same point in two views to check matches and inliers.
*`show_gt` shows the ground-truth trajectory.
*`show_edges` shows the edges from the factors. Relative-pose factors in red, roll-pitch factors in magenta and bundle adjustment co-visibility edges in green.
*`show_points` shows 3D landmarks.
The workflow for the mapping is the following:
*`detect` detect the keypoints in the keyframe images.
*`match` run the geometric 2D to 2D matching between image frames.
*`tracks` build tracks from 2D matches and triangulate the points.
**NOTE: It appears that only the datasets in ASL Dataset Format (`euroc` dataset type in our notation) contain ground truth that is time-aligned to the IMU and camera images. It is located in the `state_groundtruth_estimate0` folder. Bag files have raw Mocap measurements that are not time aligned and should not be used for evaluations.**
The visual-inertial odometry relies on the optical flow results. To enable a better analysis of the system we also provide a separate optical flow executable
We demonstrate the usage of the system with the `magistrale1` sequence of the [TUM-VI dataset](https://vision.in.tum.de/data/datasets/visual-inertial-dataset) as an example.
Download the sequence from the dataset and extract it.
