BEINGS: Bayesian Embodied Image-goal Navigation with Gaussian Splatting

TL;DR

BEINGS introduces a Bayesian, Gaussian Splatting-based approach to image-goal navigation, enabling efficient, real-time robot navigation in complex environments by formulating it as an optimal control problem within a model predictive control framework.

Contribution

It presents a novel Bayesian embodied navigation method using Gaussian Splatting as a scene prior, improving efficiency and adaptability without extensive prior data.

Findings

Effective in complex environments

Real-time navigation performance

Validated through simulations and physical experiments

Abstract

Image-goal navigation enables a robot to reach the location where a target image was captured, using visual cues for guidance. However, current methods either rely heavily on data and computationally expensive learning-based approaches or lack efficiency in complex environments due to insufficient exploration strategies. To address these limitations, we propose Bayesian Embodied Image-goal Navigation Using Gaussian Splatting, a novel method that formulates ImageNav as an optimal control problem within a model predictive control framework. BEINGS leverages 3D Gaussian Splatting as a scene prior to predict future observations, enabling efficient, real-time navigation decisions grounded in the robot's sensory experiences. By integrating Bayesian updates, our method dynamically refines the robot's strategy without requiring extensive prior experience or data. Our algorithm is validated through extensive simulations and physical experiments, showcasing its potential for embodied robot systems in visually complex scenarios.