For safe operation, a robot must be able to avoid collisions in uncertain environments. Existing approaches for motion planning under uncertainties often assume parametric obstacle representations and Gaussian uncertainty, which can be inaccurate. While visual perception can deliver a more accurate representation of the environment, its use for safe motion planning is limited by the inherent miscalibration of neural networks and the challenge of obtaining adequate datasets. To address these limitations, we propose to employ ensembles of deep semantic segmentation networks trained with massively augmented datasets to ensure reliable probabilistic occupancy information. To avoid conservatism during motion planning, we directly employ the probabilistic perception in a scenario-based path planning approach. A velocity scheduling scheme is applied to the path to ensure a safe motion despite tracking inaccuracies. We demonstrate the effectiveness of the massive data augmentation in combination with deep ensembles and the proposed scenario-based planning approach in comparisons to state-of-the-art methods and validate our framework in an experiment with a human hand as an obstacle.

翻译：为实现安全运行，机器人必须在不确定环境中避免碰撞。现有不确定性下的运动规划方法通常假设参数化障碍物表示和高斯不确定性，这种假设可能不准确。尽管视觉感知能够提供更精确的环境表示，但神经网络固有的校准偏差以及获取充足数据集的挑战限制了其在安全运动规划中的应用。为解决这些局限性，我们提出采用基于大规模增强数据集训练的深度语义分割网络集成，以确保可靠的概率占用信息。为避免运动规划中的保守性，我们直接在基于场景的路径规划方法中应用概率感知结果。通过速度调度方案对路径进行控制，可在轨迹跟踪存在误差的情况下确保安全运动。通过与现有先进方法的对比实验，我们证明了大规模数据增强与深度集成方法及所提场景规划方案的有效性，并在以人手作为障碍物的实验中验证了框架性能。