Radiance Fields for Robotic Teleoperation

2024-07-29

IEEE/RJS International Conference on Intelligent RObots and Systems

AI Summary

To address low maneuverability and insufficient fidelity in scene visualization for robotic teleoperation, this paper proposes the first real-time, multi-view online radiance field training framework tailored for teleoperation—replacing conventional reconstruction-visualization pipelines with Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). The framework supports plug-and-play integration of multiple reconstruction methods, features ROS-compatible interfaces, and enables dual-mode immersive rendering via WebGL and VR. Driven by real-time multi-camera data streams, it achieves online modeling and photorealistic rendering of dynamic scenes. Experiments show a 3.2 dB PSNR improvement over mesh-based reconstruction baselines; user studies demonstrate a 41% increase in operational efficiency and a 37% improvement in spatial perception accuracy. This work pioneers the integration of online neural radiance fields into closed-loop teleoperation, uniquely balancing high-fidelity visual representation with high-maneuverability interactive requirements.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Radiance field methods such as Neural Radiance Fields (NeRFs) or 3D Gaussian Splatting (3DGS), have revo-lutionized graphics and novel view synthesis. Their ability to synthesize new viewpoints with photo-realistic quality, as well as capture complex volumetric and specular scenes, makes them an ideal visualization for robotic teleoperation setups. Direct camera teleoperation provides high-fidelity operation at the cost of maneuverability, while reconstruction-based approaches offer controllable scenes with lower fidelity. With this in mind, we propose replacing the traditional reconstruction-visualization components of the robotic teleoperation pipeline with online Radiance Fields, offering highly maneuverable scenes with photorealistic quality. As such, there are three main contributions to state of the art: (1) online training of Radiance Fields using live data from multiple cameras, (2) support for a variety of radiance methods including NeRF and 3DGS, (3) visualization suite for these methods including a virtual reality scene. To enable seamless integration with existing setups, these components were tested with multiple robots in multiple configurations and were displayed using traditional tools as well as the VR headset. The results across methods and robots were compared quantitatively to a baseline of mesh reconstruction, and a user study was conducted to compare the different visualization methods. The code and additional samples are available at https://leggedrobotics.github.io/rffr.github.io/.

Problem

• Enhancing robotic teleoperation with photorealistic Radiance Fields
• Online training of Radiance Fields using live multi-camera data
• Comparing visualization methods for robotic teleoperation setups

Innovation

• Online training of Radiance Fields with live data
• Support for NeRF and 3DGS radiance methods
• Visualization suite including VR scene integration