Fast LiDAR Informed Visual Search in Unseen Indoor Environments

TL;DR

This paper presents a fast, map-free visual search system that uses LiDAR and visual sensing with a perception module to efficiently locate objects in unseen indoor environments, outperforming existing methods.

Contribution

It introduces a novel perception-based planning approach combining LiDAR and visual data with a new utility function for faster object search in unknown environments.

Findings

Faster object localization compared to baseline algorithms

Effective use of LiDAR and visual data for environment understanding

Validated in real-world experiments with a Spot robot

Abstract

This paper details a system for fast visual exploration 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 viewpoints. 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 viewpoints encourage the robot to explore points unlikely to be permanent 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 experiments for 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.