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.

中文翻译：

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通过多保真高斯过程在保证性能的情况下加速多目标搜索

我们考虑这样一个场景，其中配备有朝下摄像头的自动驾驶汽车在 3D 环境中运行，其任务是在环境的 2D 地板上搜索未知数量的静止目标。关键挑战是在确保高检测精度的同时最大限度地减少搜索时间。我们使用多保真高斯过程对传感场进行建模，该过程系统地描述了在离地面不同高度可用的传感信息。基于传感模型，我们设计了一种称为快速多目标搜索 (EMTS) 的新算法，该算法 (i) 解决了覆盖范围与精度之间的权衡问题：在距离地面较远的位置采样可提供更宽的视野，但测量精度较低，(ii) 在规定的精度内计算地板的占用图，并从搜索空间中快速消除未占用的区域，以及 (iii) 有效地收集目标检测所需的样本。我们对算法进行了严格的分析，并对目标检测精度和预期检测时间建立了形式化的保证。我们使用模拟的多目标搜索场景来说明该算法。