Occupancy grids are common tools used in robotics to represent the robot environment, and that may be used to plan trajectories, select additional measurements to acquire, etc. However, deriving information about those occupancy grids from sensor measurements often induce a lot of uncertainty, especially for grid elements that correspond to occluded or far away area from the robot. This means that occupancy information may be quite uncertain and imprecise at some places, while being very accurate at others. Modelling finely this occupancy information is essential to decide the optimal action the robot should take, but a refined modelling of uncertainty often implies a higher computational cost, a prohibitive feature for real-time applications. In this paper, we introduce the notion of credal occupancy grids, using the very general theory of imprecise probabilities to model occupancy uncertainty. We also show how one can perform efficient, real-time inferences with such a model, and show a use-case applying the model to an autonomous vehicle trajectory planning problem.

中文翻译：

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Credal Occupancy Grids 中的推理和决策：轨迹规划用例

占用网格是机器人技术中用于表示机器人环境的常用工具，可用于规划轨迹、选择要获取的其他测量值等。但是，从传感器测量中获取有关这些占用网格的信息通常会导致很多不确定性，尤其是对于与机器人被遮挡或远离区域相对应的网格元素。这意味着占用信息在某些地方可能非常不确定和不精确，而在其他地方则非常准确。对这种占用信息进行精细建模对于决定机器人应采取的最佳行动至关重要，但对不确定性进行精细建模通常意味着更高的计算成本，这对于实时应用来说是一个令人望而却步的特性。在本文中，我们介绍了凭证占用网格的概念，使用非常一般的不精确概率理论来模拟占用不确定性。我们还展示了如何使用这样的模型执行高效的实时推理，并展示了将该模型应用于自动驾驶车辆轨迹规划问题的用例。