Vision-Only Robot Navigation in a Neural Radiance World (2110.00168v2)

Abstract: Neural Radiance Fields (NeRFs) have recently emerged as a powerful paradigm for the representation of natural, complex 3D scenes. NeRFs represent continuous volumetric density and RGB values in a neural network, and generate photo-realistic images from unseen camera viewpoints through ray tracing. We propose an algorithm for navigating a robot through a 3D environment represented as a NeRF using only an on-board RGB camera for localization. We assume the NeRF for the scene has been pre-trained offline, and the robot's objective is to navigate through unoccupied space in the NeRF to reach a goal pose. We introduce a trajectory optimization algorithm that avoids collisions with high-density regions in the NeRF based on a discrete time version of differential flatness that is amenable to constraining the robot's full pose and control inputs. We also introduce an optimization based filtering method to estimate 6DoF pose and velocities for the robot in the NeRF given only an onboard RGB camera. We combine the trajectory planner with the pose filter in an online replanning loop to give a vision-based robot navigation pipeline. We present simulation results with a quadrotor robot navigating through a jungle gym environment, the inside of a church, and Stonehenge using only an RGB camera. We also demonstrate an omnidirectional ground robot navigating through the church, requiring it to reorient to fit through the narrow gap. Videos of this work can be found at https://mikh3x4.github.io/nerf-navigation/ .

Vision-Only Robot Navigation in a Neural Radiance World

This paper explores the utilization of Neural Radiance Fields (NeRFs) for the novel purpose of vision-only robot navigation. NeRFs, originally developed for synthetic photo-realistic image generation, represent continuous volumetric density and RGB values using a neural network, allowing the rendering of unseen viewpoints through a ray tracing methodology. In this research, NeRFs are leveraged as an environmental representation for guiding robot navigation through complex three-dimensional scenes using only onboard RGB cameras for localization.

Robotic navigation through NeRF-represented environments involves several significant challenges. Primarily, the paper proposes a trajectory optimization algorithm designed to ensure dynamic feasibility and collision avoidance when navigating the NeRF environment, represented by points of high density. The method employs a discrete time version of differential flatness, which constrains the robot's entire pose and control inputs, utilizing NeRF density fields as a collision probability metric. This approach avoids reliance on traditional discrete obstacle representations like voxel grids or mesh models, instead smoothly defining space through neural implicit functions.

Simultaneously, the paper addresses the problem of estimating the robot's six degrees of freedom (6DoF) pose within the NeRF using a vision-based state estimation pipeline. This estimation relies on a maximum likelihood estimation (MLE) framework that synthesizes expected visual representations from the NeRF given the robot's current pose hypothesis. By comparing synthesized and actual camera images, the robot iteratively updates its believed position and velocity, thus maintaining localization accurately within the NeRF.

Empirically, the paper validates its methodology in simulated environments, demonstrating successful navigation and trajectory optimization in diverse settings including a jungle gym, the interior of a church, and the monumental Stonehenge. The highlight of the research is its successful integration of trajectory planning and vision-based state estimation into a cohesive replanning loop, where a robot uses its onboard RGB feed to recalibrate its navigation path continually, adjusting to dynamic uncertainties and maintaining collision-free trajectories.

Theoretical and practical implications of this paper span various facets of autonomous robotics. Theoretically, it sets a precedent for utilizing neural implicit fields for robotics, significantly simplifying the task of environmental representation. The probabilistic interpretation of NeRF density as a collision proxy suggests new directions for integrating photorealistic rendering technologies with real-world physical interactions. Practically, it promises enhanced robotic navigation systems that forgo the need for costly and complex sensor suites, relying instead on RGB cameras and computational intensity.

Future developments based on this work could explore aspects such as semantic understanding of the NeRF-encoded environments for more intelligent navigation, real-time computational efficiency, and multi-modal sensory data integration for even higher robustness. Additionally, as improvement in real-time rendering capabilities of NeRFs progresses, the application of this framework could expand to more extensive and complex environments, presenting new opportunities in fields demanding vision-based autonomous navigation, including drone operations, rescue missions, and urban exploration tasks.