面向目标检测的带邻域随机旅行商问题
Stochastic Traveling Salesperson Problem with Neighborhoods for Object Detection
AI总结：
该研究提出面向目标检测的带邻域随机旅行商问题，采用基于熵的观测评分定义检测邻域，提出中心访问方法分别处理不相交与相交区域的路径规划，在仿真环境中验证了方法的高效性。
摘要：
我们提出了一种同时考虑感知成本与出行成本的新型路径规划问题。具体而言，我们研究的是寻找能成功检测所有感兴趣目标的最短巡游路径的问题。为表示每个目标的可行检测区域，我们提出采用基于熵的观测评分方法，生成直径有界的区域作为观测邻域。我们将基于检测的轨迹规划问题建模为带邻域随机旅行商问题，并提出一种中心访问方法，对于不相交区域可获得O(DmaxDmin)的近似比。对于相交区域，我们的方法利用区域的最小曲率特性，提出了一种新颖的三维有限绕行方案。最后，我们在照片级真实感仿真环境中验证，与基线方法相比，该方法能生成更高效的轨迹。
英文摘要：
We introduce a new route-finding problem which considers perception and travel costs simultaneously. Specifically, we consider the problem of finding the shortest tour such that all objects of interest can be detected successfully. To represent a viable detection region for each object, we propose to use an entropy-based viewing score that generates a diameter-bounded region as a viewing neighborhood. We formulate the detection-based trajectory planning problem as a stochastic traveling salesperson problem with neighborhoods and propose a center-visit method that obtains an approximation ratio of O(DmaxDmin) for disjoint regions. For non-disjoint regions, our method -provides a novel finite detour in 3D, which utilizes the region's minimum curvature property. Finally, we show that our method can generate efficient trajectories compared to a baseline method in a photo-realistic simulation environment.
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