Multi-Session Visual SLAM for Illumination Invariant Re-Localization in Indoor Environments

Abstract

For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved re-localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing re-localization performance between multi-session maps created using the RTAB-Map library with SURF, SIFT, BRIEF, BRISK, KAZE, DAISY and SuperPoint visual features. The approach is tested on six mapping and six localization sessions recorded at 30 minute intervals during sunset using a Google Tango phone in a real apartment.

20 pages, 7 figures

References in corpus (4)

• RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation
• Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation
• Long-Term Online Multi-Session Graph-Based SPLAM with Memory Management
• Predictive and adaptive maps for long-term visual navigation in changing environments

Cited by in corpus (5)

• AirSLAM: An Efficient and Illumination-Robust Point-Line Visual SLAM System
• maplab 2.0 -- A Modular and Multi-Modal Mapping Framework
• GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization
• Sensor Fusion for Autonomous Indoor UAV Navigation in Confined Spaces
• Visual Semantic Navigation with Real Robots