Autonomous agents rely on sensor data to construct representations of their environment, essential for predicting future events and planning their own actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in dynamic environments, where efficiently inferring the state of the environment based on sensor readings from different times is still an open problem. This work focuses on inferring the state of the dynamic part of the environment, i.e., where dynamic objects might be, based on previous observations and constraints on their dynamics. We formalize the problem and introduce Transitional Grid Maps (TGMs), an efficient analytical solution. TGMs are based on a set of novel assumptions that hold in many practical scenarios. They significantly reduce the complexity of the problem, enabling continuous prediction and updating of the entire dynamic map based on the known static map (see Fig.1), differentiating them from other alternatives. We compare our approach with a state-of-the-art particle filter, obtaining more prudent predictions in occluded scenarios and on-par results on unoccluded tracking.

翻译：自主智能体依赖传感器数据构建环境表征，这对于预测未来事件并规划自身行动至关重要。然而，传感器测量存在范围受限、遮挡及噪声等问题。在动态环境中，如何基于不同时刻的传感器读数高效推断环境状态仍是一个待解决的问题。本研究聚焦于基于先前的观测及其动力学约束，推断环境中动态部分（即动态物体可能存在的区域）的状态。我们形式化该问题并提出了过渡网格地图（TGMs）这一高效分析性解决方案。TGMs基于一组在众多实际场景中成立的新颖假设，显著降低了问题的复杂度，使其能够基于已知的静态地图（见图1）对完整动态地图进行连续预测与更新，从而区别于其他替代方案。我们将该方法与最先进的粒子滤波器进行对比，在遮挡场景中获得了更为审慎的预测结果，在无遮挡跟踪场景中则取得了相当的表现。