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Nova-UTD / navigator

Navigator, our self-driving vehicle software stack
https://nova-utd.github.io/navigator
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Drivable surface segmentation #372

Open danielv012 opened 10 months ago

jruths commented 1 month ago

Currently the costmap for driveable surface is provided by the map manager. While this is a reasonable place to get a driveable surface map, it tends to be slow and creates some significant lag on the realtime aggregated costmap. It also puts possibly unnecessary reliance on the map. The goal here is to use camera and possibly lidar to identify the surfaces around the vehicle that are driveable (road) so it can be used to create a costmap for road/not-road.

jruths commented 2 weeks ago

There currently exists a image_segmentation_node.py that may do some of this or provide a starting point? I don't think we currently use this node, so it is untested (or at least hasn't been tested in the past year). If it works and one of the "classes" it produces is driveable road, then creating the costmap would largely be about mapping the image into a costmap (projecting the image into 3D space... which is related to another task: #370 ).

This node is located at src/perception/segmentation/image_segmentation_node.py

saishravanm commented 5 days ago

09/09/2024 Update:

-Status: Researching how to use a Voxel Grid to convert the 2D occupancy grid (returned by the existing image_segmentation_node) into a 3D costmap. -Next Steps: Work on creating a Voxel Grid. -Projected Completion: 9/16/24 -Update: Commented and understood the existing image_segmentation_node and image_projection_node.