Gaussian-LIC：基于高斯溅射与激光雷达-惯性测量单元-相机融合的实时照片级真实感SLAM
Gaussian-LIC: Real-Time Photo-Realistic SLAM with Gaussian Splatting and LiDAR-Inertial-Camera Fusion
AI总结：
针对现有辐射场SLAM在无界场景及不利条件下的性能问题，提出Gaussian-LIC方法，通过紧耦合多传感器、融合多源点初始化3D高斯等策略，实现鲁棒位姿估计与实时照片级建图，性能优于同类方法且超过用真值位姿的对比方法。
摘要：
本文提出一种基于高斯溅射（Gaussian Splatting）与激光雷达-惯性测量单元-相机（LiDAR-Inertial-Camera）SLAM结合的实时照片级真实感SLAM方法。现有大多数基于辐射场的SLAM系统主要针对有界室内环境，配备RGB-D或RGB传感器；但扩展到无界场景或遇到剧烈运动、光照变化等不利条件时，性能易下降。相比之下，我们的方法面向通用场景，额外紧耦合激光雷达、IMU和相机，以实现鲁棒的位姿估计和照片级真实感在线建图。为补偿激光雷达未观测到的区域，我们提出融合图像三角化视觉点和激光雷达点来初始化3D高斯。此外，还实现了天空建模和可变相机曝光，以提升渲染质量。值得注意的是，我们的系统完全用C++和CUDA实现，并精心设计一系列策略加速基于高斯的场景表示的在线优化。大量实验表明，该方法在保持实时性的同时优于同类方法；令人印象深刻的是，在照片级真实感建图方面，使用我们估计的位姿的方法甚至超过所有使用特权真值位姿进行建图的对比方法。代码已发布于https://github.com/APRIL-ZJU/Gaussian-LIC。
英文摘要：
In this paper, we present a real-time photo-realistic SLAM method based on marrying Gaussian Splatting with LiDAR-Inertial-Camera SLAM. Most existing radiance-field-based SLAM systems mainly focus on bounded indoor environments, equipped with RGB-D or RGB sensors. However, they are prone to decline when expanding to unbounded scenes or encountering adverse conditions, such as violent motions and changing illumination. In contrast, oriented to general scenarios, our approach additionally tightly fuses LiDAR, IMU, and camera for robust pose estimation and photo-realistic online mapping. To compensate for regions unobserved by the LiDAR, we propose to integrate both the triangulated visual points from images and LiDAR points for initializing 3D Gaussians. In addition, the modeling of the sky and varying camera exposure have been realized for high-quality rendering. Notably, we implement our system purely with C++ and CUDA, and meticulously design a series of strategies to accelerate the online optimization of the Gaussian-based scene representation. Extensive experiments demonstrate that our method outperforms its counterparts while maintaining real-time capability. Impressively, regarding photo-realistic mapping, our method with our estimated poses even surpasses all the compared approaches that utilize privileged ground-truth poses for mapping. Our code has been released on https://github.com/APRIL-ZJU/Gaussian-LIC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
