E2O-SLAM: A Hierarchical Visual SLAM Framework Using Edge-Based and Object-Level Representations

Abstract

In this paper, we present a hierarchical simultaneous localization and mapping (SLAM) system that leverages point-level features, mid-level geometric organized edge representations, and high-level object semantics within a unified framework. While object-level SLAM provides semantic information and improves long-term data association, it often suffers from coarse geometric constraints and unreliable detections. In contrast, organized edge representations capture rich structural and textural information, offering stable geometric cues in low-texture or challenging environments. By hierarchically integrating these complementary representations, the proposed system achieves robust camera tracking, reliable data association, and consistent mapping.