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 map-generalizable 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. In order to achieve this, we propose a utility function that accounts for traditional metrics like information gain and path cost and also for the additional contextual information from the scan classifier. LIVES is baselined against several existing exploration methods in simulation to verify its performance. Finally, it is validated in real-world experiments searching for single and multiple targets with a Spot robot in two unseen environments. Videos of experimental validation, implementation details and open source code can be found on our project website at https://sites.google.com/view/lives-2024/home.

翻译：本文探讨了在没有先验地图信息的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，即给定宽视场二维扫描数据，必须执行激光雷达分割以对周围环境中的点进行上下文标注。这些像素分类为视觉搜索任务中规划下一个最佳视角提供了有依据的先验信息。我们提出 LIVES：激光雷达引导的视觉搜索方法，旨在未知室内环境中寻找感兴趣的目标。通过使用配备基于地图分类器的简易推车平台收集的专家数据，训练了一个可泛化至不同地图的分类器。自主探索规划器利用扫描数据的上下文信息，基于该先验规划更可能检测到搜索目标的视角。为此，我们提出一种效用函数，综合考虑信息增益、路径成本等传统指标，以及来自扫描分类器的额外上下文信息。在仿真中，我们将 LIVES 与多种现有探索方法进行基准测试以验证其性能。最终，我们使用 Spot 机器人在两个未知环境中进行单目标和多目标搜索的真实世界实验验证。实验验证视频、实施细节及开源代码可在项目网站 https://sites.google.com/view/lives-2024/home 获取。