Visuelle Navigation und Lokalisation in einem Raum mithilfe von Neural Radiance Fields und Gaussian Splatting

The suitability of Neural Radiance Fields and Gaussian Splatting for visual‑only localisation and navigation of indoor mobile robots is investigated. A Monte‑Carlo particle filter that compares camera images with synthetically rendered views is implemented on a TurtleBot and evaluated at a minimum update rate of 1 Hz, demonstrating that accurate global localisation can be achieved without prior pose information, though occasional localisation errors remain. The results suggest that a fully visual, low‑cost navigation system based on these radiance‑field models is feasible for real‑time robot operation.