LiDAR-enhanced 3D Gaussian Splatting Mapping (2503.05425v1)

Abstract: This paper introduces LiGSM, a novel LiDAR-enhanced 3D Gaussian Splatting (3DGS) mapping framework that improves the accuracy and robustness of 3D scene mapping by integrating LiDAR data. LiGSM constructs joint loss from images and LiDAR point clouds to estimate the poses and optimize their extrinsic parameters, enabling dynamic adaptation to variations in sensor alignment. Furthermore, it leverages LiDAR point clouds to initialize 3DGS, providing a denser and more reliable starting points compared to sparse SfM points. In scene rendering, the framework augments standard image-based supervision with depth maps generated from LiDAR projections, ensuring an accurate scene representation in both geometry and photometry. Experiments on public and self-collected datasets demonstrate that LiGSM outperforms comparative methods in pose tracking and scene rendering.

• The paper introduces LiGSM, a framework that fuses LiDAR data with 3D Gaussian Splatting using a joint loss function and improved initialization for more accurate and robust 3D mapping.
• Experimental results show LiGSM outperforms existing methods, achieving an average ATE of 4.77 cm on TUM RGB-D and superior rendering quality metrics like PSNR, SSIM, and LPIPS.
• The integration of LiDAR enhances mapping accuracy and robustness, with significant implications for autonomous driving and robotics by providing reliable environmental models.

LiDAR-enhanced 3D Gaussian Splatting Mapping: A Comprehensive Overview

The paper presents an advanced mapping framework titled LiGSM, which integrates LiDAR data into the 3D Gaussian Splatting (3DGS) technique to enhance accuracy and robustness in 3D scene mapping. LiGSM represents a significant step forward in the field by addressing the inherent limitations of traditional 3DGS, which relies heavily on visual data and often encounters challenges with sparse and unreliable point cloud initialization.

Core Contributions and Methodology

LiGSM's primary objective is to leverage the precision offered by LiDAR, known for its resilience to environmental conditions like lighting variance and its ability to capture detailed spatial information over extensive ranges. The inclusion of LiDAR in the mapping process is characterized by several strategic enhancements:

1. Joint Loss Construction: LiGSM formulates a joint loss function that assimilates information from both images and LiDAR point clouds. This design is pivotal as it facilitates the estimation of poses and the dynamic optimization of extrinsic parameters, thereby aligning sensor inputs and adapting to variations in sensor alignment dynamically.
2. Initialization Improvements: By initializing the 3DGS with dense LiDAR point clouds instead of sparse SfM points, LiGSM provides a more reliable starting point, enhancing the geometric fidelity of the scene representation.
3. Enhanced Scene Rendering: The framework introduces depth maps derived from LiDAR projections into the rendering process. This dual-supervision approach, which combines image-based supervision with LiDAR-derived depth maps, ensures that both the geometric and photometric characteristics of the scene are accurately represented.

Experimental Evaluation

The paper includes extensive experimental evaluations using both public and self-collected datasets. These experiments demonstrate that LiGSM outperforms comparative methods in terms of pose tracking and scene rendering. Specific metrics, such as absolute trajectory error (ATE) for tracking performance and PSNR, SSIM, and LPIPS for image rendering quality, underscore the superior results of LiGSM. For instance, the system achieves an average ATE of 4.77 cm on the TUM RGB-D dataset, significantly outperforming other methods like SplaTAM.

Implications and Future Directions

The integration of LiDAR in 3DGS not only augments the mapping accuracy but also enhances the overall robustness of the scene reconstruction. From a practical standpoint, this fusion has substantial implications for fields such as autonomous driving and robotics, where reliable and precise environmental modeling is critical. Theoretically, the framework opens avenues for exploring multi-modal data fusion in 3D mapping, pushing the boundaries of current state-of-the-art models.

Future developments could potentially focus on improving the scalability of LiGSM in larger, more complex environments, as well as optimizing the computational efficiency to support real-time applications. The continued exploration of the harmony between visual and LiDAR data could yield further improvements in both the quality and reliability of 3D scene reconstructions.

In conclusion, the LiGSM framework illustrates a compelling advancement in 3D scene mapping by incorporating LiDAR data into the 3D Gaussian Splatting methodology. This innovation not only addresses prior limitations associated with pure visual dependency but also sets a new standard in terms of accuracy and robustness, making it a significant contribution to the field of computer vision and robotic perception.