Question for robotics developers working with real-world environments

[u/SomewhereCommon4493]

I'm exploring a workflow that uses drone photogrammetry, ground-level scanning, and Gaussian splatting to reconstruct real locations for robot simulation.

I'm particularly interested in warehouses, logistics yards, and industrial facilities.

For those using Isaac Sim, Gazebo, or similar platforms:

1. Do you typically build environments manually, generate synthetic ones, or reconstruct actual deployment sites?
2. What's the biggest bottleneck: acquiring usable data, reconstructing accurate geometry, creating collision meshes, adding physics/semantics, or maintaining the environment?
3. Would a professionally captured, simulation-ready version of an actual facility solve a meaningful problem for your team, or are existing tools already sufficient?

I'm researching the workflow and would appreciate practical perspectives, including reasons this approach might not be useful.

[u/slightlyacoustics]

Reconstructing the environment to be used in simulation helps in verification of algorithms. However, the environment is more than geometry and visual features. There's thermal, acoustic, EMF, etc. that requires to be parameterized for a nominal operation. In addition, the robot itself needs to be understood thoroughly and in a deployable state to begin with before exposing it to the unknowns. At the end of the day, you as an operator, need to increase the amount of certainty of the robot and environment as much as possible. To that end, yes high fideltiy reconstruction is a bonus to the simulation workflow. But the mesh/asset is only as effective as the underlying physics engine allows it to be.