Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environment are statically and which parts are dynamically occupied. We formulate the problem as a Bayesian network and introduce minimal assumptions that significantly reduce the complexity of the problem. Based on those, we derive Transitional Grid Maps (TGMs), an efficient analytical solution. Using real data, we demonstrate how this approach produces better maps by keeping track of both static and dynamic elements and, as a side effect, can help improve existing SLAM algorithms.

翻译：自主智能体依赖传感器数据构建环境表征，这对预测未来事件和规划行动至关重要。然而，传感器测量存在范围有限、遮挡和传感器噪声等问题。这些挑战在高度动态的环境中尤为显著。本研究提出一种概率框架，用于联合推断环境的哪些部分为静态占用，哪些部分为动态占用。我们将该问题建模为贝叶斯网络，并引入最小化假设以显著降低问题复杂度。基于此，我们推导出过渡网格地图（TGMs）——一种高效解析解。通过真实数据验证，我们展示了该方法如何通过同时追踪静态与动态要素来生成更优地图，并作为附加效益有助于改进现有SLAM算法。