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.

翻译：定位与建图是自动驾驶和机器人等各类应用中的关键任务。户外环境因其无界特性带来的挑战尤为复杂。本文提出MM-Gaussian，一种面向无界场景中定位与建图的激光雷达-相机多模态融合系统。该方法受近期发展的三维高斯技术启发，该技术在高渲染质量与快速渲染速度方面展现出卓越能力。具体而言，本系统充分利用固态激光雷达提供的几何结构信息，以解决在无界户外场景中仅依赖视觉方案时遇到的深度不准确问题。同时，借助像素级梯度下降，利用三维高斯点云充分挖掘照片中的色彩信息，从而实现逼真的渲染效果。为增强系统鲁棒性，我们设计了重定位模块，可在定位失败时辅助系统回归正确轨迹。多场景实验验证了该方法的有效性。