GS-LIVM: Real-Time Photo-Realistic LiDAR-Inertial-Visual Mapping with Gaussian Splatting (2410.17084v1)

Abstract: In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments. In this work, Gaussian Process Regression (GPR) is employed to mitigate the issues resulting from sparse and unevenly distributed LiDAR observations. The voxel-based 3D Gaussians map representation facilitates real-time dense mapping in large outdoor environments with acceleration governed by custom CUDA kernels. Moreover, the overall framework is designed in a covariance-centered manner, where the estimated covariance is used to initialize the scale and rotation of 3D Gaussians, as well as update the parameters of the GPR. We evaluate our algorithm on several outdoor datasets, and the results demonstrate that our method achieves state-of-the-art performance in terms of mapping efficiency and rendering quality. The source code is available on GitHub.

• The paper introduces GS-LIVM, a method that fuses LiDAR, inertial, and visual data with Gaussian Splatting to achieve photo-realistic mapping.
• It employs voxel-level Gaussian Process Regression to generate uniform point clouds, reducing GPU memory use and accelerating 3D Gaussian optimization.
• Experiments demonstrate that GS-LIVM outperforms previous approaches in rendering metrics like PSNR, SSIM, and LPIPS in large-scale outdoor environments.

Overview of GS-LIVM: Real-Time Photo-Realistic LiDAR-Inertial-Visual Mapping with Gaussian Splatting

The paper presents GS-LIVM, a real-time method for photo-realistic mapping in large-scale unbounded outdoor environments. Unlike traditional approaches relying on NeRF and 3DGS, GS-LIVM integrates LiDAR, inertial, and visual data, introducing Gaussian Splatting to enhance mapping efficiency and rendering quality.

Methodology

The key innovation of GS-LIVM lies in its employment of Gaussian Process Regression (GPR) at the voxel level to address issues related to the sparse and uneven distribution of LiDAR observations. This approach allows for the construction of a uniform point cloud mesh grid, reducing GPU memory consumption and accelerating the optimization of 3D Gaussians.

The framework operates in a covariance-centered manner, utilizing estimated covariances to initialize and update the scale and rotation of the 3D Gaussians. This covariance framework facilitates efficient and accurate mapping in real-time. Moreover, the incorporation of custom CUDA kernels further enhances the acceleration of real-time dense mapping.

The system is structured around several components, including:

1. Voxel-GPR: A novel method for generating evenly distributed point clouds, mitigating the challenges posed by the sparsity of LiDAR data.
2. Efficient Initialization: A method for rapidly converging the parameters of 3D Gaussians, critical for maintaining rendering quality during fast movements.
3. Iterative Optimization Framework: Leveraging variance estimates and image rendering data to refine 3D photo-realistic reconstructions.

Experimentation and Results

The paper evaluates the GS-LIVM framework across various outdoor datasets, demonstrating state-of-the-art performance in both mapping efficiency and rendering quality. The results indicate significant improvements over existing techniques, particularly in handling large-scale environments.

Quantitatively, the method yields strong performance in terms of rendering metrics such as PSNR, SSIM, and LPIPS, across challenging datasets. The framework also manages computational resources effectively, maintaining real-time operations on hardware with limited GPU capacity.

Implications and Future Directions

This research contributes to the field by advancing real-time SLAM methodologies, particularly for outdoor scenes. The integration of Gaussian Splatting with LiDAR-Inertial-Visual data represents a significant step forward. By reducing memory consumption and enhancing rendering speed, GS-LIVM opens new possibilities for deploying real-time photo-realistic mapping in various applications including autonomous driving and robotics.

Future explorations could address the limitations noted in initializing the 3DGS model in sparse data regions and further refining rendering quality. The extension to incorporate more diverse sensor inputs and improve adaptability to various terrains and environments is a potential development area, maintaining the momentum towards comprehensive autonomous systems.

Overall, GS-LIVM stands as a robust framework that effectively tackles the real-time mapping challenges posed by complex, unbounded outdoor settings, providing a foundation for future enhancements in autonomous mapping technologies.