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.

翻译：我们提出了WildFusion，一种利用多模态隐式神经表示在非结构化野外环境中进行三维场景重建的新方法。WildFusion整合了来自LiDAR、RGB相机、接触式麦克风、触觉传感器和IMU的信号。这种多模态融合生成了全面、连续的环境表示，包括像素级几何、颜色、语义和可通行性。通过在具有挑战性的森林环境中对腿式机器人导航进行的真实世界实验，WildFusion通过准确预测可通行性，展示了其在路径选择方面的改进。我们的结果突显了其在复杂户外地形中推动机器人导航和三维建图方面的潜力。