论文标题
基于概率语义细分的基于视觉的不确定性感知运动计划
Vision-Based Uncertainty-Aware Motion Planning based on Probabilistic Semantic Segmentation
论文作者
论文摘要
对于安全操作，机器人必须能够避免在不确定的环境中发生碰撞。在不确定性下进行运动计划的现有方法通常假设参数障碍物表示和高斯不确定性，这可能是不准确的。虽然视觉感知可以对环境提供更准确的表示，但其用于安全运动计划的使用受到神经网络的固有错误校准以及获得足够数据集的挑战的限制。为了解决这些限制，我们建议采用经过大量增强数据集训练的深层语义分割网络的集合，以确保可靠的概率占用信息。为了避免在运动计划中的保守主义，我们直接在基于情况的路径计划方法中采用了概率感知。速度调度方案被应用于路径上，以确保跟踪不准确的情况。我们证明了大规模数据增强与深层合奏结合的有效性以及与最先进方法相比的基于方案的计划方法，并在人类手作为障碍的实验中验证了我们的框架。
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.
