Radiance Fields for Robotic Teleoperation (2407.20194v1)

Abstract: Radiance field methods such as Neural Radiance Fields (NeRFs) or 3D Gaussian Splatting (3DGS), have revolutionized 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. For videos and code, check out https://leggedrobotics.github.io/rffr.github.io/.

• The paper introduces online training of Radiance Fields using live multi-camera data to enhance robotic teleoperation visualization.
• It compares NeRF and 3D Gaussian Splatting with traditional mesh methods, showing significant improvements in fidelity and performance.
• User studies confirm that VR-based Radiance Field visualizations significantly boost teleoperation precision and immersive control.

Radiance Fields for Robotic Teleoperation

Overview

The paper "Radiance Fields for Robotic Teleoperation" by Maximum Wilder-Smith, Vaishakh Patil, and Marco Hutter investigates the integration of Radiance Field methods, such as Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), into robotic teleoperation. The system proposed aims to enhance the visualization quality and maneuverability of teleoperation setups by introducing online Radiance Fields. These fields are trained using live data from multiple cameras mounted on robots, offering photorealistic quality and dynamic scene representation. The contributions of the paper include:

1. Online training of Radiance Fields with live multi-camera data.
2. Support for various Radiance Field methods.
3. A comprehensive visualization suite, including virtual reality (VR) support.

Methodology

The described pipeline involves three main components: the robot, reconstruction methods, and the visualization system. Data is captured from robots of varying configurations, including static arms and mobile platforms. This data is then processed through different reconstruction methods, with a comparison made between a traditional mesh-based approach (Voxblox) and Radiance Field methods (NeRF and 3DGS).

Robots

The paper tests multiple robotic setups: a static arm, a mobile quadruped, and a mobile arm attached to a quadruped. These setups serve varying degrees of scene complexity and size, from constrained areas captured with static arms to large, dynamic environments explored by mobile bases.

Reconstruction Methods

The core innovation lies in the reconstruction methods employed:

• NeRF: Utilizes a multi-layer perceptron (MLP) to render new views from sparse images. NeRF is noted for its high-quality results but slower rendering times.
• 3DGS: Uses an explicit representation of radiance fields with 3D Gaussians, achieving efficient computation and rendering, making it suitable for real-time applications.

Both methods are integrated into a ROS-compatible Radiance Field node, ensuring interoperability with existing robotic systems and visualization tools.

Visualization

For effective teleoperation, high-fidelity visualization is paramount. The paper presents:

• RViz Plugin: A plugin that integrates with ROS, supporting dynamic and continuous modes of operation, including depth-based occlusion and scene cropping.
• VR Visualization: A VR suite that allows immersive control and interaction with the robot in a virtually reconstructed environment. This suite offers both a 2.5D handheld viewer and a fully immersive 360-degree view.

Experimental Results

Dataset and Quality Evaluation

The system was tested on datasets captured from static and mobile robotic setups. Quality metrics such as peak-signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS) were used for evaluation. Results indicated that Radiance Fields provided significantly higher fidelity reconstructions compared to traditional mesh methods. Notably, NeRF and 3DGS outperformed the mesh method in all metrics, with 3DGS achieving real-time rendering speeds.

Performance

Performance benchmarks showed that Radiance Field methods achieved faster reconstruction and rendering times than traditional methods. For NeRF and 3DGS, training times to a target quality (16.94 dB PSNR) were significantly lower, and 3DGS maintained near-constant rendering times across resolutions, ensuring suitability for online use.

User Study

A user paper involving 20 participants demonstrated a preference for VR-based Radiance Field visualizations over traditional methods for tasks requiring high perception and manipulation precision. The paper showed that VR systems provided enhanced usability and immersion, suggesting that integrating Radiance Fields in VR could provide substantial benefits for robotic teleoperation.

Implications and Future Work

The integration of Radiance Fields into robotic teleoperation represents a significant step toward achieving high-fidelity, maneuverable, and immersive teleoperation systems. The methods proposed demonstrate the potential for improved situational awareness and control precision, which are critical in complex and dynamic environments.

Future research may explore direct 3D representation of Gaussians in VR, further optimizing the performance and quality of Radiance Fields, and extending the system's capabilities to more diverse robotic applications.

Conclusion

The paper provides a robust and adaptable pipeline for integrating Radiance Fields into robotic teleoperation, leveraging the latest advancements in neural rendering and immersive visualization. The presented system not only achieves superior reconstruction quality but also offers a scalable and efficient approach for real-time applications. This work paves the way for more immersive and accurate teleoperation systems, enhancing human-robot interaction in increasingly complex environments.