In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation accuracy. vSLAM can use feature points or a sequence of images, as it is the only source of input that can perform localization while simultaneously creating a map of the environment. A vSLAM system based on ORB-SLAM3 and on YOLOR was proposed in this paper. The newly proposed system in combination with an object detection model (YOLOX) applied on extracted feature points is capable of achieving 2–4% better accuracy compared to VPS-SLAM and DS-SLAM. Static feature points such as signs and benches were used to calculate the camera position, and dynamic moving objects were eliminated by using the tracking thread. A specific custom personal dataset that includes indoor and outdoor RGB-D pictures of train stations, including dynamic objects and high density of people, ground truth data, sequence data, and video recordings of the train stations and X, Y, Z data was used to validate and evaluate the proposed method. The results show that ORB-SLAM3 with YOLOR as object detection achieves 89.54% of accuracy in dynamic indoor environments compared to previous systems such as VPS-SLAM.

This paper undertakes a comprehensive investigation that surpasses the conventional examination of signal enhancement techniques and their effects on visual Simultaneous Localization and Mapping (vSLAM) performance across diverse scenarios. Going beyond the conventional scope, the study extends its focus towards the seamless integration of signal enhancement techniques, aiming to achieve a substantial enhancement in the overall vSLAM performance. The research not only delves into the assessment of existing methods but also actively contributes to the field by proposing innovative denoising techniques that can play a pivotal role in refining the accuracy and reliability of vSLAM systems. This multifaceted approach encompasses a thorough exploration of the intricate relationships between signal enhancement, denoising strategies, their cumulative impact on the performance of vSLAM in real-world applications and the innovative use of Generative Adversarial Networks (GANs) for image inpainting. The GANs effectively fill in missing spaces following object detection and removal, presenting a novel state-of-the-art approach that significantly enhances overall accuracy and execution speed of vSLAM. This paper aims to contribute to the advancement of vSLAM algorithms in real-world scenarios, demonstrating improved accuracy, robustness, and computational efficiency through the amalgamation of signal enhancement and advanced denoising techniques.