We present Splatblox, a real-time system for autonomous navigation in outdoor environments with dense vegetation, irregular obstacles, and complex terrain. Our method fuses segmented RGB images and LiDAR point clouds using Gaussian Splatting to construct a traversability-aware Euclidean Signed Distance Field (ESDF) that jointly encodes geometry and semantics. Updated online, this field enables semantic reasoning to distinguish traversable vegetation (e.g., tall grass) from rigid obstacles (e.g., trees), while LiDAR ensures 360-degree geometric coverage for extended planning horizons. We validate Splatblox on a quadruped robot and demonstrate transfer to a wheeled platform. In field trials across vegetation-rich scenarios, it outperforms state-of-the-art methods with over 50% higher success rate, 40% fewer freezing incidents, 5% shorter paths, and up to 13% faster time to goal, while supporting long-range missions up to 100 meters. Experiment videos and more details can be found on our project page: https://splatblox.github.io

翻译：本文提出Splatblox，一种用于密集植被、不规则障碍物和复杂地形等室外环境的实时自主导航系统。该方法通过高斯泼溅技术融合分割后的RGB图像与LiDAR点云，构建一个同时编码几何与语义信息的可通行性感知欧几里得符号距离场。该场在线更新，支持语义推理以区分可通行植被（如高草）与刚性障碍物（如树木），而LiDAR则提供360度几何覆盖以扩展规划视野。我们在四足机器人上验证了Splatblox，并展示了其向轮式平台的迁移能力。在植被丰富的场景实地测试中，该系统优于现有最优方法：成功率提高超过50%，冻结事件减少40%，路径缩短5%，目标到达时间加快最高达13%，同时支持长达100米的远距离任务。实验视频及更多细节请访问项目页面：https://splatblox.github.io