Planning to Perceive: Exploiting Mobility for Robust Object Detection
Planning to Perceive: Exploiting Mobility for Robust Object Detection
复制标题
规划感知：利用移动性实现稳健的物体检测
DOI：
--
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
N. Roy
中科院分区：
文献类型：
--
作者：
Javier Vélez;Garrett Hemann;Albert S. Huang;I. Posner;N. Roy
考虑一个移动机器人在环境中自主导航的任务，同时使用噪声对象检测器检测和映射感兴趣的对象。机器人必须及时到达目的地，但如果正确检测到可识别的物体并将其添加到地图上，则会获得奖励，如果出现错误警报则会受到惩罚。然而，探测器的性能通常会随着有利位置的变化而变化，因此机器人受益于规划轨迹，从而最大化识别系统的效率。这项工作描述了一个在线的、任何时间的计划框架，它能够主动探索由现成的对象检测器提供的可能的检测。我们提出了一种概率方法，其中确定了有利位置，从而提供了对潜在对象的更多信息视图。然后，代理权衡增加其信心的收益与绕道到达每个确定的有利位置的成本。仿真实验和真实机器人实验表明，该系统显著提高了机器人的检测速度和轨迹长度。
Consider the task of a mobile robot autonomously navigating through an environment while detecting and mapping objects of interest using a noisy object detector. The robot must reach its destination in a timely manner, but is rewarded for correctly detecting recognizable objects to be added to the map, and penalized for false alarms. However, detector performance typically varies with vantage point, so the robot benefits from planning trajectories which maximize the efficacy of the recognition system. This work describes an online, any-time planning framework enabling the active exploration of possible detections provided by an off-the-shelf object detector. We present a probabilistic approach where vantage points are identified which provide a more informative view of a potential object. The agent then weighs the benefit of increasing its confidence against the cost of taking a detour to reach each identified vantage point. The system is demonstrated to significantly improve detection and trajectory length in both simulated and real robot experiments.