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MM-Gaussian: 3D Gaussian-based Multi-modal Fusion for Localization and Reconstruction in Unbounded Scenes (2404.04026v1)

Published 5 Apr 2024 in cs.RO and cs.CV

Abstract: Localization and mapping are critical tasks for various applications such as autonomous vehicles and robotics. The challenges posed by outdoor environments present particular complexities due to their unbounded characteristics. In this work, we present MM-Gaussian, a LiDAR-camera multi-modal fusion system for localization and mapping in unbounded scenes. Our approach is inspired by the recently developed 3D Gaussians, which demonstrate remarkable capabilities in achieving high rendering quality and fast rendering speed. Specifically, our system fully utilizes the geometric structure information provided by solid-state LiDAR to address the problem of inaccurate depth encountered when relying solely on visual solutions in unbounded, outdoor scenarios. Additionally, we utilize 3D Gaussian point clouds, with the assistance of pixel-level gradient descent, to fully exploit the color information in photos, thereby achieving realistic rendering effects. To further bolster the robustness of our system, we designed a relocalization module, which assists in returning to the correct trajectory in the event of a localization failure. Experiments conducted in multiple scenarios demonstrate the effectiveness of our method.

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Citations (8)
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Summary

  • The paper presents a novel LiDAR-camera fusion that leverages 3D Gaussian point clouds for enhanced SLAM in expansive outdoor settings.
  • It introduces a relocalization module that corrects trajectory deviations, significantly improving localization accuracy and mapping fidelity.
  • Empirical results highlight its superiority over legacy SLAM methods, offering clear advancements for autonomous navigation and outdoor mapping.

MM-Gaussian: Advancing Localization and Mapping in Unbounded Scenes with 3D Gaussian-based Multi-modal Fusion

Introduction

The domain of Simultaneous Localization and Mapping (SLAM) has witnessed substantial progress, evolving to meet the complex demands of applications like autonomous vehicles and robotics. However, the quest for enhanced precision and realism in mapping, particularly in expansive outdoor settings, remains challenging. The recently introduced MM-Gaussian approach marks a significant step forward, deploying a LiDAR-camera multi-modal fusion system. Leveraging the strengths of both technologies, MM-Gaussian efficiently addresses the depth inaccuracies typical of visual solutions in unbounded scenarios, utilizing 3D Gaussian point clouds for increasingly realistic rendering effects. This combination not only enhances geometric structural information capture but also renders high-quality images, bolstered further by a unique relocalization module designed to improve system robustness by correcting trajectory deviations.

Key Contributions

The MM-Gaussian system embodies several notable advancements:

  • The integration of solid-state LiDAR with cameras facilitates high-precision localization and mapping across vast outdoor scenes, a leap forward from the limitations posed by existing RGB-D or monocular camera-based methods.
  • A novel relocalization module enhances the system's resilience against localization failures, leveraging rendered images from Gaussians for trajectory correction.
  • Empirical evaluations underscore the superiority of MM-Gaussian over legacy 3D Gaussians SLAM methods, particularly in localization accuracy and mapping fidelity.

Methodological Overview

Tracking and Relocalization

MM-Gaussian introduces an efficient tracking phase, utilizing point cloud registration to estimate the sensor's pose accurately, which is crucial for integrating sensor data into 3D Gaussian maps. The system's robustness is further ensured by a relocalization module that addresses tracking failures often encountered in complex scenes, such as those with textureless surfaces. This module uses a “look-around” operation, assisting in reorienting the trajectory toward the correct path upon detection of a tracking anomaly.

Mapping Enhancements

The dual inputs from LiDAR and camera are synthesized for map expansion, with LiDAR point clouds converted into 3D Gaussians and incrementally integrated into the map based on the camera's pose. Map updating is performed by optimizing the attributes of Gaussians using keyframe sequences, thereby refining the map's fidelity over time. Notably, the system incorporates mechanisms for pruning ineffective Gaussians and densifying the map representation to capture finer details.

Practical Implications and Future Directions

MM-Gaussian's ability to deliver real-time rendering of high-quality images in unbounded scenes positions it as a pivotal innovation for outdoor mapping and localization. Its implementation can significantly enhance autonomous navigation systems, among other applications. The introduction of a relocalization module underscores the potential of rendering-based approaches for improving SLAM system resilience. Looking ahead, there is vast potential for further optimizing the accuracy and efficiency of such fusion-based methods, which could broaden their applicability and performance in real-world scenarios.

Conclusion

The MM-Gaussian method represents a substantial advancement in SLAM technology, pushing the boundaries of what is achievable in outdoor localization and mapping. Its innovative use of 3D Gaussian-based multi-modal fusion, coupled with a unique relocalization module, signifies a notable leap toward solving the complexities of mapping unbounded outdoor environments with unprecedented realism and accuracy. As SLAM systems continue to evolve, approaches like MM-Gaussian offer a glimpse into the future of autonomous navigation and beyond.

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