LITE: A Learning-Integrated Topological Explorer for Multi-Floor Indoor Environments

TL;DR

This paper introduces LITE, a novel learning-integrated topological exploration framework for multi-floor indoor environments, combining 2D exploration methods with a floor-stair topology for efficient 3D exploration and real-world applicability.

Contribution

LITE uniquely integrates 2D exploration methods with a floor-stair topology and attention-based policies for effective multi-floor indoor exploration.

Findings

Outperforms baseline explorers in exploration efficiency.

Compatible with various 2D exploration methods.

Validated in real-world quadruped robot experiments.

Abstract

This work focuses on multi-floor indoor exploration, which remains an open area of research. Compared to traditional methods, recent learning-based explorers have demonstrated significant potential due to their robust environmental learning and modeling capabilities, but most are restricted to 2D environments. In this paper, we proposed a learning-integrated topological explorer, LITE, for multi-floor indoor environments. LITE decomposes the environment into a floor-stair topology, enabling seamless integration of learning or non-learning-based 2D exploration methods for 3D exploration. As we incrementally build floor-stair topology in exploration using YOLO11-based instance segmentation model, the agent can transition between floors through a finite state machine. Additionally, we implement an attention-based 2D exploration policy that utilizes an attention mechanism to capture spatial dependencies between different regions, thereby determining the next global goal for more efficient exploration. Extensive comparison and ablation studies conducted on the HM3D and MP3D datasets demonstrate that our proposed 2D exploration policy significantly outperforms all baseline explorers in terms of exploration efficiency. Furthermore, experiments in several 3D multi-floor environments indicate that our framework is compatible with various 2D exploration methods, facilitating effective multi-floor indoor exploration. Finally, we validate our method in the real world with a quadruped robot, highlighting its strong generalization capabilities.