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I guess we figured out the mechanics, and, to my opinion, it's not very intuitive.

After voxel marking occurs, the mark_threshold parameter counts occupied voxels in each single vertical stack, and marks the corresponding 2d cell as occupied if the total number of occupied voxels exceeds the parameter.

For our case, this changes almost nothing, since many obstacles are only marked by a single horizontal scanning plane of our lidar. Manipulating the observation_persistence parameter does not change things much; apparently, it only affects marking of single voxels.

I think the functionality I asked for in the question would actually be a valuable addition to costmap_2d. When working with dense clouds (which happens more and more often) this could be a basis of effective dynamic filtering and proofing of obstacles.

We will have to use another way of handling obstacles, possibly octomap.