论文标题
加急多目标搜索，并通过多保真高斯流程保证了性能
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes
论文作者
论文摘要
我们考虑了一种场景，在3D环境中，配备了向下摄像机的自动驾驶汽车运行，并负责在环境的2楼搜索未知数量的固定目标。关键挑战是在确保高检测准确性的同时最大程度地减少搜索时间。我们使用多忠诚的高斯工艺对传感场进行建模，该过程系统地描述了地板不同高度处可用的传感信息。 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有效地收集所需的样品以进行目标检测。我们严格分析算法并在目标检测准确性和预期检测时间上建立正式保证。我们使用模拟的多目标搜索方案说明了算法。
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.
