SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection (2403.06877v1)

Abstract: We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures. This system adapts the state-of-the-art neural radiance field (NeRF) representation to also incorporate lidar data which adds strong geometric constraints on the depth and surface normals. We exploit the trajectory from a real-time lidar SLAM system to bootstrap a Structure-from-Motion (SfM) procedure to both significantly reduce the computation time and to provide metric scale which is crucial for lidar depth loss. We use submapping to scale the system to large-scale environments captured over long trajectories. We demonstrate the reconstruction system with data from a multi-camera, lidar sensor suite onboard a legged robot, hand-held while scanning building scenes for 600 metres, and onboard an aerial robot surveying a multi-storey mock disaster site-building. Website: https://ori-drs.github.io/projects/silvr/

• The paper advances 3D reconstruction by integrating lidar with NeRF to enhance geometric fidelity and capture detailed textures.
• It employs dynamic lidar SLAM and trajectory bootstrapping to significantly reduce computation time while ensuring global scale accuracy.
• The submapping strategy enables scalable mapping over large areas, validated through diverse real-world robotic inspection tests.

Overview of SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection

The paper presents SiLVR, an advanced reconstruction system designed to integrate lidar and visual data utilizing Neural Radiance Fields (NeRFs) for generating high-quality, scalable 3D reconstructions. This system addresses traditional challenges in robotic inspections, where dense 3D reconstructions are vital for tasks such as industrial inspection and autonomous navigation.

Key Contributions

SiLVR innovatively adapts the state-of-the-art NeRF representations to include lidar data, which enhances geometric constraints and improves the accuracy of depth and surface normal estimations. The integration of lidar data allows the system to maintain robust performance in texture-less areas—where typical vision-based methods might falter.

Major Contributions:

1. Integrated Lidar and Visual Based 3D Reconstructions: SiLVR combines multi-camera visual data with lidar measurements to construct photorealistic 3D models that match the geometric fidelity provided by lidar while benefiting from texture detail by cameras.
2. Geometric Constraints from Lidar: The system includes both depth and surface normal regularization from lidar to strengthen the geometry reconstruction process. These additions help mitigate the challenges NeRF faces in areas of limited texture and inadequate multi-view input.
3. Efficient Trajectory Bootstrapping for Enhanced Mapping: By utilizing a real-time lidar SLAM system to drive a Structure-from-Motion (SfM) process, the computation time is significantly reduced while still ensuring the global metric scale necessary for practical applications. This approach allows dynamic pose estimation, aligning lidar and visual inputs effectively.
4. Submapping for Scalability: SiLVR employs a submapping strategy to handle large-scale environments. This technique partitions the scene into local submaps, enabling the system to maintain high performance over a 600-meter trajectory without sacrificing accuracy or precision.
5. Real-world Location Tests on Diverse Platforms: The robustness of the system is demonstrated across various environments, from static scanning with handheld devices to more complex scenes using legged and aerial robots. This diversity of testing showcases SiLVR's versatility and effectiveness across different robotic configurations and mission profiles.

Implications and Future Work

The integration of NeRF with lidar data in SiLVR represents a significant advancement in large-scale 3D reconstructions, particularly for robotics applications that require accurate and detailed environmental mapping. This methodology lays groundwork for future developments in combining different sensor modalities to overcome the limitations seen when deploying individual sensors.

Future Directions:

• Further research could focus on refining the computational efficiency of integrating multiple sensor inputs to support real-time application scenarios.
• Development of adaptive approaches for handling varying conditions of light and texture to enhance the performance of inspection robots under dynamic environmental conditions.
• Exploration of new algorithms that can dynamically balance trade-offs between processing power, scalability, and reconstruction fidelity.

SiLVR is an insightful contribution to robotic inspection technology. It addresses many challenges that have hindered progress in the field, posing a significant step forward in creating robust, scalable systems for automated environments. The adoption of a neural-field-based approach to incorporation of lidar data into high-quality visual reconstructions points toward a promising horizon for efficiently mapping complex scenes in robotics.