用高斯点阵融合视觉与激光，让机器人在复杂户外环境自主导航更安全高效。
Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation
- 融合图像与激光数据构建可通行性感知的欧式距离场
- 野外测试中成功率超50%，冻结次数减少40%
- 适合四足和轮式机器人，支持百米级长距任务
我们提出Splatblox，一个用于密集植被、不规则障碍物和复杂地形的室外环境自主导航的实时系统。该方法利用高斯点阵融合分割后的RGB图像与激光雷达点云，构建一种融合几何与语义的可通行性感知欧氏有符号距离场（ESDF）。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（如树木），同时激光雷达确保360度几何覆盖，实现更长规划时域。我们在四足机器人上验证了Splatblox，并展示其迁移到轮式平台的能力。在植被丰富的实地测试中，相比现有最优方法，成功率提升超过50%，冻结事件减少40%，路径缩短5%，到达目标时间最快提升13%，并支持长达100米的远距离任务。更多实验视频与细节请见项目主页：https://splatblox.github.io
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。