Recent Advances in Visual SLAM: Taxonomy, Comparative Analysis, and Open Challenges

Abstract

Visual Simultaneous Localization and Mapping (SLAM) has become a cornerstone in the development of intelligent systems capable of perceiving and interacting with their environment in real time. This survey presents a comprehensive review of recent advances in visual SLAM algorithms, with a focus on their classification, performance characteristics, and application domains. This study categorizes existing methods into monocular, stereo, RGB-D, and multi-sensor/hybrid approaches, analyzing key contributions such as ORB-SLAM, DSO, ElasticFusion, and VINS-Mono. Each class is evaluated in terms of accuracy, robustness, and computational efficiency while highlighting the trade-offs associated with different sensor modalities. Additionally, this study explores cross-modal and deep learning-based hybrid SLAM systems, which incorporate semantic understanding, motion segmentation, and sensor fusion to enhance performance in complex and dynamic environments. Application areas, including robotics, augmented/virtual reality, 3D mapping, and wearable technologies, are discussed to underscore the practical relevance of visual SLAM. Finally, the survey outlines the main challenges and future directions, including lifelong mapping, real-time performance on edge devices, semantic integration, and the emergence of SLAM 2.0 systems. This work aims to serve as a resource for researchers and practitioners seeking to understand the state of the art and guide future innovation in the field of visual SLAM.