Fast LiDAR Informed Visual Search in Unseen Indoor Environments

This paper details a system for fast visual explo- ration and search without prior map information. We leverage frontier based planning with both LiDAR and visual sensing and augment it with a perception module that contextually labels points in the surroundings from wide Field of View 2D LiDAR scans. The goal of the perception module is to recognize surrounding points more likely to be the search target in order to provide an informed prior on which to plan next best view- points. The robust map-free scan classifier used to label pixels in the robot’s surroundings is trained from expert data collected using a simple cart platform equipped with a map-based classifier. We propose a novel utility function that accounts for the contextual data found from the classifier. The resulting view- points encourage the robot to explore points unlikely to be per- manent in the environment, leading the robot to locate objects of interest faster than several existing baseline algorithms. Our proposed system is further validated in real-world search exper- iments for single and multiple search objects with a Spot robot in two unseen environments. Videos of experiments, implemen- tation details and open source code can be found at https: //sites.google.com/view/lives-2024/home.