This paper explores the problem of planning for visual search without prior map information. We leverage the pixel-wise environment perception problem where one is given wide Field of View 2D scan data and must perform LiDAR segmentation to contextually label points in the surroundings. These pixel classifications provide an informed prior on which to plan next best viewpoints during visual search tasks. We present LIVES: LiDAR Informed Visual Search, a method aimed at finding objects of interest in unknown indoor environments. A robust map-free classifier is trained from expert data collected using a simple cart platform equipped with a map-based classifier. An autonomous exploration planner takes the contextual data from scans and uses that prior to plan viewpoints more likely to yield detection of the search target. We propose a utility function that accounts for traditional metrics like information gain and path cost and for the contextual information. LIVES is baselined against several existing exploration methods in simulation to verify its performance. It is validated in real-world experiments with single and multiple search objects with a Spot robot in two unseen environments. Videos of experiments, implementation details and open source code can be found at https://sites.google.com/view/lives-2024/home.

翻译：本文探索了在无先验地图信息情况下规划视觉搜索的问题。我们利用像素级环境感知问题，即在获得宽视场二维扫描数据时，必须执行LiDAR分割以对周围点进行上下文标记。这些像素分类为视觉搜索任务中规划下一最佳视点提供了信息先验。我们提出LIVES：LiDAR引导的视觉搜索方法，旨在未知室内环境中寻找目标物体。基于配备地图分类器的简易推车平台采集的专家数据，训练了鲁棒的无地图分类器。自主探索规划器利用扫描数据中的上下文信息，基于该先验规划更有可能检测到搜索目标的视点。我们提出一个效用函数，综合考虑信息增益、路径代价等传统指标及上下文信息。在仿真实验中，将LIVES与多种现有探索方法进行基线对比以验证性能。通过搭载Spot机器人在两个未知环境中进行包含单目标和多目标的真实世界实验验证。实验视频、实施细节及开源代码可在https://sites.google.com/view/lives-2024/home 获取。