SLAM for Visually Impaired People: a Survey
Marziyeh Bamdad, Davide Scaramuzza, Alireza Darvishy
TL;DR
The paper surveys 54 SLAM-based navigation studies for visually impaired people published from 2017 to mid-2023, systematically categorizing localization/mapping techniques, sensor types, computing resources, and machine-learning methods. It finds a dominant use of visual SLAM (e.g., ORB-SLAM variants) often combined with IMU/depth data, and a trend toward semantic mapping and platform-specific SLAM implementations (e.g., Cartographer, ARCore, OpenVSLAM). The study analyzes advantages (infrastructure-free operation, real-time localization, map reuse) and limitations (computational demands, feature-poor environments, drift, dynamic scenes), then evaluates challenges (crowded spaces, illumination changes, practical usability) and real-world effectiveness via user studies. Finally, it outlines opportunities (robust multi-sensor fusion, deep learning integration, indoor–outdoor unification, realistic datasets, and improved HCI) and emphasizes translating prototypes into market-ready, user-centered solutions with standardized evaluation frameworks.
Abstract
In recent decades, several assistive technologies have been developed to improve the ability of blind and visually impaired (BVI) individuals to navigate independently and safely. At the same time, simultaneous localization and mapping (SLAM) techniques have become sufficiently robust and efficient to be adopted in developing these assistive technologies. We present the first systematic literature review of 54 recent studies on SLAM-based solutions for blind and visually impaired people, focusing on literature published from 2017 onward. This review explores various localization and mapping techniques employed in this context. We systematically identified and categorized diverse SLAM approaches and analyzed their localization and mapping techniques, sensor types, computing resources, and machine-learning methods. We discuss the advantages and limitations of these techniques for blind and visually impaired navigation. Moreover, we examine the major challenges described across studies, including practical challenges and considerations that affect usability and adoption. Our analysis also evaluates the effectiveness of these SLAM-based solutions in real-world scenarios and user satisfaction, providing insights into their practical impact on BVI mobility. The insights derived from this review identify critical gaps and opportunities for future research activities, particularly in addressing the challenges presented by dynamic and complex environments. We explain how SLAM technology offers the potential to improve the ability of visually impaired individuals to navigate effectively. Finally, we present future opportunities and challenges in this domain.