LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments (2411.12185v1)

Abstract: We present LiV-GS, a LiDAR-visual SLAM system in outdoor environments that leverages 3D Gaussian as a differentiable spatial representation. Notably, LiV-GS is the first method that directly aligns discrete and sparse LiDAR data with continuous differentiable Gaussian maps in large-scale outdoor scenes, overcoming the limitation of fixed resolution in traditional LiDAR mapping. The system aligns point clouds with Gaussian maps using shared covariance attributes for front-end tracking and integrates the normal orientation into the loss function to refines the Gaussian map. To reliably and stably update Gaussians outside the LiDAR field of view, we introduce a novel conditional Gaussian constraint that aligns these Gaussians closely with the nearest reliable ones. The targeted adjustment enables LiV-GS to achieve fast and accurate mapping with novel view synthesis at a rate of 7.98 FPS. Extensive comparative experiments demonstrate LiV-GS's superior performance in SLAM, image rendering and mapping. The successful cross-modal radar-LiDAR localization highlights the potential of LiV-GS for applications in cross-modal semantic positioning and object segmentation with Gaussian maps.

• The paper introduces a unified SLAM framework that integrates LiDAR and vision using 3D Gaussian splatting for improved outdoor mapping.
• It leverages a normal-direction and conditional Gaussian constraint to enhance alignment and map accuracy in challenging environments.
• The system achieves real-time view synthesis at 7.98 FPS, outperforming traditional approaches in complex outdoor scenarios.

LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments

The paper "LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments" presents a novel approach to Simultaneous Localization and Mapping (SLAM) which combines LiDAR and visual data to optimize 3D scene reconstruction. This integration leverages 3D Gaussian splatting as a spatial representation to address the complexities of outdoor environments, presenting improvements over traditional SLAM methodologies constrained by fixed resolutions and high computational demands.

LiV-GS is distinguished by its use of Gaussian ellipsoids to represent space adaptively, aligning discrete and sparse LiDAR data with continuous Gaussian maps. This is achieved using covariance information for the initial alignment and updates the map using a novel conditional Gaussian constraint. The SLAM framework enhances mapping accuracy, yielding new view synthesis at an actionable rate of 7.98 FPS, which is significant for real-time applications.

Methodology and Contributions

This research introduces several innovations within the SLAM framework:

1. Unified LiDAR-Camera SLAM Framework: The paper proposes a SLAM system that utilizes 3D Gaussian distributions, allowing for incremental mapping. This results in high-quality view synthesis alongside positioning.
2. Gaussian-LiDAR Alignment: The method employs a normal-direction constraint for stable tracking, supplemented by a weighting mechanism that considers density and normal consistency to evaluate Gaussian reliability.
3. Conditional Gaussian Constraint: This constraint enables the propagation of reliable Gaussian representations, ensuring that map regions absent from LiDAR data are adequately reconstructed and aligned.

The integration of LiDAR point clouds into a Gaussian framework mitigates common issues in outdoor SLAM such as depth continuity and lighting variations. Specifically, the Gaussian splitting method based on LiDAR data corrects depth inaccuracies, enabling robust scene reconstruction.

Numerical Results and Evaluation

Through extensive experiments, LiV-GS demonstrates superior accuracy in localization and mapping tasks compared to existing SLAM methods. These include comparisons with NeRF-LOAM and other Gaussian-based approaches. The system's efficacy is emphasized by its performance in mapping and image rendering across multiple outdoor sequences.

Particularly noteworthy is the successful implementation of cross-modal radar-LiDAR localization, underscoring the effective spatial representation by the Gaussian maps even when LiDAR data is intermittently unavailable.

Implications and Future Developments

LiV-GS contributes significantly to the field of SLAM by advancing the integration of LiDAR and visual data into a cohesive framework. The implications for real-time SLAM applications are profound, given the improved processing speeds and mapping accuracy demonstrated.

The framework's reliance on Gaussian representations presents opportunities for advancing research in cross-modal localization and semantic mapping. Future developments could explore loop closure techniques and refined color modeling to further enhance the system's robustness and accuracy, particularly in unbounded and looped environments.

Additionally, the introduction of a conditional Gaussian constraint sets a novel precedent for adapting SLAM systems to handle spatially varied environments where traditional methods struggle.

In conclusion, LiV-GS represents an advancement in the integration of visual and LiDAR data within a SLAM framework, offering a path forward for applications requiring precise mapping and localization in dynamic outdoor environments.