基于多保真度高斯过程的快速多目标搜索
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes
摘要：
我们考虑一个场景，其中配备有向下的摄像头的自动驾驶车辆在3D环境中运行，并负责在环境的2D地板上搜索未知数量的固定目标。关键的挑战是最大限度地减少搜索时间，同时确保高检测精度。我们使用多保真度高斯过程，系统地描述了在不同的高度从地板上的传感信息的传感场建模。基于感知模型，我们设计了一种称为加速多目标搜索（EMTS）的新算法，该算法（i）解决了覆盖率-准确度的权衡：在离地板较远的位置处采样提供了较宽的视场但较不精确的测量，（ii）在规定的精确度内计算地板的占用图并从搜索空间中快速消除未占用区域，以及（iii）有效地行进以收集用于目标检测的所需样本。我们严格地分析了算法，并建立正式的目标检测精度和预期的检测时间的保证。我们使用模拟的多目标搜索场景来说明该算法。
英文摘要：
We consider a scenario in which an autonomous vehicle equipped with a downward facing camera operates in a 3D environment and is tasked with searching for an unknown number of stationary targets on the 2D floor of the environment. The key challenge is to minimize the search time while ensuring a high detection accuracy. We model the sensing field using a multi-fidelity Gaussian process that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i) addresses the coverage-accuracy trade-off: sampling at locations farther from the floor provides wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection. We rigorously analyze the algorithm and establish formal guarantees on the target detection accuracy and the expected detection time. We illustrate the algorithm using a simulated multi-target search scenario.
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