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Mapping and Localization

✎

Modified 2021-07-14 by Andrea Censi

The Duckiedrone can perform localization with a map created from an image, as well as the ability to run SLAM (online or offline). Both methods use the drone’s camera to produce a pose estimate, but localization requires a map of the environment before hand, represented as images stitched together (map.jpg) whereas SLAM builds a map of the environment as it runs. SLAM does not run well online even with a fast base station. However it gives good performance when run offline to create a map, and then later using that map to localize.

These scripts replace the vision_flow_and_phase.py script that does velocity and position control. The reason is that we save time and memory by avoiding sending images to different nodes on the Pi and instead do all processing in a single node that directly connects to the camera.

The recommended workflow is to first run SLAM to create a map offline. Then after creating the map, run localization with the saved map to give the drone a global position estimate.

SLAM

✎

Modified 2021-07-14 by Andrea Censi

The recommended mode for SLAM is to run it offline; there is not enough compute even offboard to run it during flight. It may be possible to run SLAM online with additional optimization or by rewriting the algorithm in C++; for this reason we include instructions for running online as well. Note that if you run anything offboard, ROS must be installed on the offboard machine. Running offboard is not required to run SLAM; however it may be significantly faster to create the map if you use a fast base station.

Offline SLAM:

Save Flight Data: The first step to offline SLAM is to fly the drone and collect image data to build the map. To do this, run ‘vision_localization_offboard.py –offline’ which will save the image data from a flight to ‘flight_data.txt.’ Press ‘r’ to toggle recording the data. After the second press, the script will write the data to the file and will let you know when it is done (this may take ~10 min for a longer flight).

Build a Map: This can be done either offline or online. Run “offline_slam.py” which will look for the “flight_data.txt” file and save a map to a file called “map.txt.”

Localize: Currently this only works onboard, but we will add offboard support soon. Make sure your “map.txt” file is in the scripts folder and run “vision_localization_onboard.py –read” which will perform localization over the map created by SLAM. Press “r” to restart the localization.

Online SLAM Online SLAM runs but not in real time, even offboard. Therefore we do not recommend it until and unless we make it fast enough to run in realtime. However we are including instructions in case someone wants to try! For online slam:

Offboard: run vision_localization_offboard.py on the pi. On the offboard computer run offboard_slam.py. Press r to toggle SLAM.

Onboard: run vision_localization_onboard.py --SLAM on the pi. Press r to toggle SLAM.

Localization with a Stitched Map

✎

Modified 2021-07-14 by Andrea Censi

These instructions describe how to create a stitched image of a map with your cell phone or other camera. Once you have created a map, you can use it to localize.

Take photos of the new map with a cell phone or other camera. Take them at a height of pointed downward at the workspace. Use an image stitching software to generate the map. We recommend auto-stitch or hugin. Replace map.jpg with your new map and change the following four parameters in offboard_localization.py, onboard_localization.py, and localization_helper.py: MAP_PIXEL_WIDTH, MAP_PIXEL_HEIGHT, MAP_REAL_WIDTH, MAP_REAL_HEIGHT. You may need to resize the image to be smaller if it is too large.

Onboard: run vision_localization_onboard.py on the pi. You must fly over the area captured in map.jpg. Press r in the web interface to toggle localization.

Offboard: run vision_localization_offboard.py on the pi and offboard_localization.py on the remote computer. You must fly over the area captured in map.jpg. Press r to toggle localization.