Demonstrations
In this wiki page, we show how to apply the SegMap algorithm on different datasets and for different applications. This page will be updated over time with new examples.
This demonstration shows how to reproduce the results of the first part of the SegMap video.
To download all necessary files, copy the contents of the segmap_data into ~/.segmap/.
Once these file are copied (~/.segmap/kitti should be a valid path), you can start the demonstration using:
$ roslaunch segmapper kitti_localization.launch
The rosbag will first be paused to give the program some time to load the point cloud from disk and to extract and describe segments. Once that is completed, you should see the segments from the target point cloud in white, as illustrated in the following image.
Once segmentation of the target cloud is completed, you can return to the shell where the file was launched and start playing the bag by hitting the space bar. As the vehicle moves, you should start observing localizations as displayed in the following image.
In this image, the segments extracted from the source cloud are represented with random colors. The green lines indicate segments matches which passed the geometrical verification step. The vehicle trajectory is illustrated in orange and the red spheres indicate where segmentation occurred.
On some machines, the visualization of the point cloud might significantly affect performances. For optimal performances, you can try subscribing only to the segments centroid clouds eg.: /segmap/source_segments_centroids and /segmap/target_segments_centroids. Please note that, for this demonstration, the target point cloud and segment centroids are published only once. For you information, the input of the SegMap algorithm is a local map created by the laser_mapper on a topic called /segmapper/local_map.
This demonstration shows how to reproduce the results of the second part of the SegMap video. In this scenario, the target segment map is built online and the robot is able to find and close loops based on segment matching.
The demonstration can be launched using:
$ roslaunch segmapper kitti_loop_closure.launch
Pressing the space bar will then play the bag and you should see the target map being incrementally built as shown in white in the following figure.
Once the robot revisits a part of the environment you should observe segment matches and loop closures displayed in green and blue respectively. The trajectory is optimized by feeding the loop-closures in a sparse pose-graph optimizer as illustrated in the following figure.
This final figure illustrates how the trajectory should look like once the bag has completed.
As mentioned above, the visualization of the point cloud might significantly affect performances. For optimal performances, you can try subscribing only to the segments centroid clouds eg.: /segmap/source_segments_centroids and /segmap/target_segments_centroids.
More demonstrations to come :-)