CATNIPS: Collision Avoidance Through Neural Implicit Probabilistic Scenes

doi:10.1109/TRO.2024.3386394

CATNIPS: Collision Avoidance Through Neural Implicit Probabilistic Scenes

2024-01-01

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DOI：10.1109/TRO.2024.3386394

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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 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.

Keywords:

Robots

Collision avoidance

Trajectory

Cameras

Probabilistic logic

Planning

Three-dimensional displays

neural radiance fields (NeRFs)

robot safety

visual-based navigation

CATNIPS: Collision Avoidance Through Neural Implicit Probabilistic Scenes

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