用高斯点云实现轻量级激光雷达定位与建图，效率优于现有方法。
Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping
- 基于高斯点云直接建模，仅用激光雷达数据优化点云
- 定位精度媲美当前最佳，建图效果达到新标杆
- 低显存占用，适合实时机器人应用
激光雷达提供精确的几何测量，在自运动估计和场景重建中具有重要价值。然而，构建准确且轻量的环境表示仍具挑战，传统方法与基于NeRF的方法均需在精度、内存和计算时间之间权衡。本文基于高斯点云最新进展，提出一种全新的激光雷达里程计与建图流水线，全程仅使用高斯原型进行场景表示。通过球面投影，仅利用激光雷达数据对高斯点云进行优化。实验表明，该方法在配准性能上达到当前水平，同时在建图任务中实现最先进结果，且对GPU资源需求极低。这一高效性使其成为实时机器人估计任务中极具潜力的候选方案。
原文摘要 · Abstract (English)
LiDARs provide accurate geometric measurements, making them valuable for ego-motion estimation and reconstruction tasks. Although its success, managing an accurate and lightweight representation of the environment still poses challenges. Both classic and NeRF-based solutions have to trade off accuracy over memory and processing times. In this work, we build on recent advancements in Gaussian Splatting methods to develop a novel LiDAR odometry and mapping pipeline that exclusively relies on Gaussian primitives for its scene representation. Leveraging spherical projection, we drive the refinement of the primitives uniquely from LiDAR measurements. Experiments show that our approach matches the current registration performance, while achieving SOTA results for mapping tasks with minimal GPU requirements. This efficiency makes it a strong candidate for further exploration and potential adoption in real-time robotics estimation tasks.
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