融合多传感器信号，实现野外复杂环境的连续3D场景重建
WildFusion: Multimodal Implicit 3D Reconstructions in the Wild
- 结合激光雷达、相机、触觉传感器等多模态数据构建隐式3D表示
- 在森林环境中实现精准通行性预测，提升机器人路径选择能力
- 适合需要高精度户外导航的机器人系统研究者参考
我们提出WildFusion，一种用于非结构化野外环境的3D场景重建新方法，采用多模态隐式神经表征。该方法融合激光雷达、RGB相机、接触式麦克风、触觉传感器和惯性测量单元（IMU）信号，生成包含像素级几何、颜色、语义与通行性的完整连续环境表征。在复杂森林环境下对足式机器人导航的真实世界实验表明，该方法能准确预测通行性，显著改善路径规划效果。结果证明其在复杂室外地形中推进机器人导航与三维建图的潜力。
原文摘要 · Abstract (English)
We propose WildFusion, a novel approach for 3D scene reconstruction in unstructured, in-the-wild environments using multimodal implicit neural representations. WildFusion integrates signals from LiDAR, RGB camera, contact microphones, tactile sensors, and IMU. This multimodal fusion generates comprehensive, continuous environmental representations, including pixel-level geometry, color, semantics, and traversability. Through real-world experiments on legged robot navigation in challenging forest environments, WildFusion demonstrates improved route selection by accurately predicting traversability. Our results highlight its potential to advance robotic navigation and 3D mapping in complex outdoor terrains.
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