相关论文: SLAM for Visually Impaired People: a Survey

People who are blind employ unique strategies when performing instrumental activities of daily living (iADLs), often relying on multiple sensory modalities and assistive technologies. While prior research has extensively explored adaptive…

Navigation solutions suitable for cases when both autonomous robot's pose (\textit{i.e}., attitude and position) and its environment are unknown are in great demand. Simultaneous Localization and Mapping (SLAM) fulfills this need by…

Classic vision-based navigation solutions, which are utilized in algorithms such as Simultaneous Localization and Mapping (SLAM), usually fail to work underwater when the water is murky and the quality of the recorded images is low. That is…

The rapid growth of virtual reality (VR) has led to increased use of social VR platforms for interaction. However, these platforms lack adequate features to support blind and low vision (BLV) users, posing significant challenges in…

Simultaneous localization and mapping (SLAM) is used to predict the dynamic motion path of a moving platform based on the location coordinates and the precise mapping of the physical environment. SLAM has great potential in augmented…

Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information. On the other hand, state of the art…

Navigating unfamiliar environments remains one of the most persistent and critical challenges for people who are blind or have limited vision (BLV). Existing assistive tools often rely on online services or APIs, making them costly,…

Existing solutions to visual simultaneous localization and mapping (V-SLAM) assume that errors in feature extraction and matching are independent and identically distributed (i.i.d), but this assumption is known to not be true -- features…

Distributed as an open source library since 2013, RTAB-Map started as an appearance-based loop closure detection approach with memory management to deal with large-scale and long-term online operation. It then grew to implement Simultaneous…

Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication…

Simultaneous Localization and Mapping (SLAM) plays a crucial role in enabling autonomous vehicles to navigate previously unknown environments. Semantic SLAM mostly extends visual SLAM, leveraging the higher density information available to…

Simultaneous localization and mapping (SLAM) is a fundamental task for numerous applications such as autonomous navigation and exploration. Despite many SLAM datasets have been released, current SLAM solutions still struggle to have…

Blind people face significant challenges when using smartphones. The focus on improving non-visual mobile accessibility has been at the level of touchscreen access. Our research investigates the challenges faced by blind people in their…

In today's society, where independent living is becoming increasingly important, it can be extremely constricting for those who are blind. Blind and visually impaired (BVI) people face challenges because they need manual support to prompt…

This survey paper presents a comprehensive overview of the latest advancements in the field of Simultaneous Localization and Mapping (SLAM) with a focus on the integration of symbolic representation of environment features. The paper…

This paper proposes a novel visual simultaneous localization and mapping (SLAM) system called Hybrid Depth-augmented Panoramic Visual SLAM (HDPV-SLAM), that employs a panoramic camera and a tilted multi-beam LiDAR scanner to generate…

This paper demonstrates a system capable of combining a sparse, indirect, monocular visual SLAM, with both offline and real-time Multi-View Stereo (MVS) reconstruction algorithms. This combination overcomes many obstacles encountered by…

Large Vision-Language Models (LVLMs) demonstrate a promising direction for assisting individuals with blindness or low-vision (BLV). Yet, measuring their true utility in real-world scenarios is challenging because evaluating whether their…

Robust Visual SLAM (vSLAM) is essential for autonomous systems operating in real-world environments, where challenges such as dynamic objects, low texture, and critically, varying illumination conditions often degrade performance. Existing…

Approximately 200 million individuals around the world suffer from varying degrees of visual impairment, making it crucial to leverage AI technology to offer walking assistance for these people. With the recent progress of vision-language…