We present DyFOS, an active perception method that dynamically finds optimal states to minimize localization uncertainty while avoiding obstacles and occlusions. We consider the scenario where a perception-denied rover relies on position and uncertainty measurements from a viewer robot to localize itself along an obstacle-filled path. The position uncertainty from the viewer's sensor is a function of the states of the sensor itself, the rover, and the surrounding environment. To find an optimal sensor state that minimizes the rover's localization uncertainty, DyFOS uses a localization uncertainty prediction pipeline in an optimization search. Given numerous samples of the states mentioned above, the pipeline predicts the rover's localization uncertainty with the help of a trained, complex state-dependent sensor measurement model (a probabilistic neural network). Our pipeline also predicts occlusion and obstacle collision to remove undesirable viewer states and reduce unnecessary computations. We evaluate the proposed method numerically and in simulation. Our results show that DyFOS is faster than brute force yet performs on par. DyFOS also yielded lower localization uncertainties than faster random and heuristic-based searches.

翻译：我们提出DyFOS，一种主动感知方法，能够动态寻找最优传感器状态，在避开障碍物和遮挡的同时最小化定位不确定性。考虑如下场景：一台感知受限的巡视器需要依赖观测机器人的位置及不确定性测量结果，沿着布满障碍物的路径实现自身定位。观测器传感器的位置不确定性是传感器自身状态、巡视器状态及周围环境状态的函数。为寻找能最小化巡视器定位不确定性的最优传感器状态，DyFOS采用定位不确定性预测管线进行优化搜索。该管线基于上述状态的大量样本，借助经过训练的复杂状态依赖型传感器测量模型（概率神经网络）预测巡视器的定位不确定性。同时，该管线还预测遮挡和障碍物碰撞，以剔除不可取的观测器状态并减少不必要的计算。我们通过数值仿真与模拟仿真评估所提方法。结果表明，DyFOS的速度虽不及暴力搜索法但性能相当，且相较于更快的随机搜索与基于启发式的搜索，DyFOS获得了更低的定位不确定性。