仅用单目图像实现避障，无需显式环境建模
Collision avoidance from monocular vision trained with novel view synthesis
- 用2D高斯点阵生成逼真图像训练避障策略
- 真实场景中在拦截路径下仍能有效避障
- 适合无精确定位的移动机器人应用
碰撞避让通常依赖于显式环境模型（如高程图或占用网格），但需精准状态估计。本文提出从隐式环境模型进行避障。使用单目RGB图像作为输入，通过2D高斯点阵生成的逼真图像训练避障策略。在真实世界实验中，机器人以使自身与障碍物形成拦截路径的速度指令运行。结果表明，仅凭RGB图像即可在训练所在房间及分布外环境中做出有效避障决策。
原文摘要 · Abstract (English)
Collision avoidance can be checked in explicit environment models such as elevation maps or occupancy grids, yet integrating such models with a locomotion policy requires accurate state estimation. In this work, we consider the question of collision avoidance from an implicit environment model. We use monocular RGB images as inputs and train a collisionavoidance policy from photorealistic images generated by 2D Gaussian splatting. We evaluate the resulting pipeline in realworld experiments under velocity commands that bring the robot on an intercept course with obstacles. Our results suggest that RGB images can be enough to make collision-avoidance decisions, both in the room where training data was collected and in out-of-distribution environments.
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