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：我们提出Splatblox——一种在密集植被、不规则障碍物和复杂地形的户外环境中实现自主导航的实时系统。本方法利用高斯泼溅融合分割后的RGB图像与激光雷达点云，构建了联合编码几何与语义的可通过性感知欧几里得符号距离场（ESDF）。该字段在线更新，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），同时激光雷达确保360度几何覆盖以扩展规划视界。我们在四足机器人上验证了Splatblox，并展示了向轮式平台的迁移能力。在植被密集场景的野外试验中，本方法优于现有技术：成功率提升超50%，冻结事件减少40%，路径缩短5%，到达目标时间加快最高13%，同时支持最远100米的长距离任务。实验视频及更多详情请访问项目页面：https://splatblox.github.io