Topology-Based Visual Active Room Segmentation

Abstract

Room segmentation plays a significant role in scene understanding, semantic mapping, and scene coverage for robots navigating in real-world indoor environments. However, most previous works take a passive segmentation that requires a complete and uncluttered grid map as input, often resulting in lower segmentation accuracy and cannot be deployed in unknown environments. In this paper, we propose an active room segmentation framework that can enable a robot to incrementally and autonomously perform room segmentation in cluttered indoor environments. Our framework consists of three key components: i) a door extraction module where a visual semantic feature, specifically, door, is extracted to better identify rooms in cluttered environments, ii) a within-room exploration module that detects frontiers within the currently exploring room, and iii) a topological module that represents connectivity between rooms and determines next room for exploration. We show through experiments that the proposed method depicts two distinct advantages against existing methods in segmentation accuracy and autonomy. The code is available at https://github.com/FreeformRobotics/Active_room_segmentation.