Simultaneous Detection of Loop-Closures and Changed Objects
This addresses the problem of computational and perceptual complexity in time-critical vSLAM applications for robotics, though it appears incremental as it builds on existing loop-closure detection methods.
The paper tackles the challenge of performing loop-closure detection and image change detection in non-stationary environments for robotic vSLAM without offline background modeling, resulting in a maintenance-free framework that reuses loop-closure detection for change detection with minimal extra cost.
Loop-closure detection (LCD) in large non-stationary environments remains an important challenge in robotic visual simultaneous localization and mapping (vSLAM). To reduce computational and perceptual complexity, it is helpful if a vSLAM system has the ability to perform image change detection (ICD). Unlike previous applications of ICD, time-critical vSLAM applications cannot assume an offline background modeling stage, or rely on maintenance-intensive background models. To address this issue, we introduce a novel maintenance-free ICD framework that requires no background modeling. Specifically, we demonstrate that LCD can be reused as the main process for ICD with minimal extra cost. Based on these concepts, we develop a novel vSLAM component that enables simultaneous LCD and ICD. ICD experiments based on challenging cross-season LCD scenarios validate the efficacy of the proposed method.