CATNIPS: Collision Avoidance Through Neural Implicit Probabilistic Scenes

@misc{chen_catnips_2023,
  title = {{CATNIPS}: {Collision} {Avoidance} {Through} {Neural} {Implicit} {Probabilistic} {Scenes}},
  url = {https://arxiv.org/abs/2302.12931},
  abstract = {We introduce a transformation of a Neural Radiance Field (NeRF) to an equivalent Poisson Point Process (PPP). This PPP transformation allows for rigorous quantification of uncertainty in NeRFs, in particular, for computing collision probabilities for a robot navigating through a NeRF environment. The PPP is a generalization of a probabilistic occupancy grid to the continuous volume and is fundamental to the volumetric ray-tracing model underlying radiance fields. Building upon this PPP representation, we present a chance-constrained trajectory optimization method for safe robot navigation in NeRFs. Our method relies on a voxel representation called the Probabilistic Unsafe Robot Region (PURR) that spatially fuses the chance constraint with the NeRF model to facilitate fast trajectory optimization. We then combine a graph-based search with a spline-based trajectory optimization to yield robot trajectories through the NeRF that are guaranteed to satisfy a user-specific collision probability. We validate our chance constrained planning method through simulations and hardware experiments, showing superior performance compared to prior works on trajectory planning in NeRF environments.},
  author = {Chen, Timothy and Culbertson, Preston and Schwager, Mac},
  month = nov,
  year = {2023},
  note = {Under Review},
  month_numeric = {11}
}