Learned Contextual LiDAR Informed Visual Search in Unseen Environments

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.