Related papers: MmWave Mapping and SLAM for 5G and Beyond
A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather…
We study landmark-based SLAM with unknown data association: our robot navigates in a completely unknown environment and has to simultaneously reason over its own trajectory, the positions of an unknown number of landmarks in the…
The main contribution of this paper is a new submap joining based approach for solving large-scale Simultaneous Localization and Mapping (SLAM) problems. Each local submap is independently built using the local information through solving a…
In this work, we propose a novel approach for high accuracy user localization by merging tools from both millimeter wave (mmWave) imaging and communications. The key idea of the proposed solution is to leverage mmWave imaging to construct a…
Simultaneous Localization and Mapping (SLAM) have made the real-time dense reconstruction possible increasing the prospects of navigation, tracking, and augmented reality problems. Some breakthroughs have been achieved in this regard during…
In recent decades, the field of robotic mapping has witnessed widespread research and development in LiDAR (Light Detection And Ranging)-based simultaneous localization and mapping (SLAM) techniques. In this paper, we review the…
When adapting Simultaneous Mapping and Localization (SLAM) to real-world applications, such as autonomous vehicles, drones, and augmented reality devices, its memory footprint and computing cost are the two main factors limiting the…
Visual SLAM (Simultaneous Localization and Mapping) based on planar features has found widespread applications in fields such as environmental structure perception and augmented reality. However, current research faces challenges in…
The LIght Detection And Ranging (LiDAR) sensor has become one of the most important perceptual devices due to its important role in simultaneous localization and mapping (SLAM). Existing SLAM methods are mainly developed for mechanical…
Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for multi-robot applications in environments where absolute positioning systems are not available. Being visual, it relies on cameras, cheap, lightweight…
In future wireless networks, the availability of information on the position of mobile agents and the propagation environment can enable new services and increase the throughput and robustness of communications. Multipath-based simultaneous…
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…
Precise indoor localization remains a challenging problem for a variety of essential applications. A promising approach to address this problem is to exchange radio signals between mobile agents and static physical anchors (PAs) that bounce…
Simultaneous Localization and Mapping (SLAM) is one of the most essential techniques in many real-world robotic applications. The assumption of static environments is common in most SLAM algorithms, which however, is not the case for most…
As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation mobile communication systems have been conducted. Future mobile communication systems are…
A novel simultaneous localization and radio mapping (SLARM) framework for communication-aware connected robots in the unknown indoor environment is proposed, where the simultaneous localization and mapping (SLAM) algorithm and the global…
With the deepening of research on the SLAM system, the possibility of cooperative SLAM with multi-robots has been proposed. This paper presents a map matching and localization approach considering the cooperative SLAM of an aerial-ground…
Simultaneous localization and mapping (SLAM) using automotive radar sensors can provide enhanced sensing capabilities for autonomous systems. In SLAM applications, with a greater requirement for the environment map, information on the…
Simultaneous localization and mapping (SLAM) is the task of building a map representation of an unknown environment while at the same time using it for positioning. A probabilistic interpretation of the SLAM task allows for incorporating…
For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has to deal with unknown initial positioning caused by either the kidnapped robot problem or multi-session mapping. This paper addresses these problems by…