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Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress…

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…

Robotics · Computer Science 2023-11-02 Xiangdi Yue , Yihuan Zhang , Miaolei He

In this paper, we present a comprehensive investigation of the challenges of Monocular Visual Simultaneous Localization and Mapping (vSLAM) methods for underwater robots. While significant progress has been made in state estimation methods…

Robotics · Computer Science 2025-07-29 Michele Grimaldi , David Nakath , Mengkun She , Kevin Köser

The real-world deployment of fully autonomous mobile robots depends on a robust SLAM (Simultaneous Localization and Mapping) system, capable of handling dynamic environments, where objects are moving in front of the robot, and changing…

Visual Simultaneous Localization and Mapping (VSLAM) research faces significant challenges due to fragmented toolchains, complex system configurations, and inconsistent evaluation methodologies. To address these issues, we present…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Alejandro Fontan , Tobias Fischer , Javier Civera , Michael Milford

Simultaneous Localization and Mapping (SLAM) and Multi-Object Tracking (MOT) are pivotal tasks in the realm of autonomous driving, attracting considerable research attention. While SLAM endeavors to generate real-time maps and determine the…

Robotics · Computer Science 2025-08-15 Peilin Tian , Hao Li

Traditional monocular Visual Simultaneous Localization and Mapping (vSLAM) systems can be divided into three categories: those that use features, those that rely on the image itself, and hybrid models. In the case of feature-based methods,…

Robotics · Computer Science 2022-10-31 Andreas Georgis , Panagiotis Mermigkas , Petros Maragos

Simultaneous Localization and Mapping (SLAM) is a key tool for monitoring construction sites, where aligning the evolving as-built state with the as-planned design enables early error detection and reduces costly rework. LiDAR-based SLAM…

Visual Simultaneous Localization and Mapping (SLAM) plays a vital role in real-time localization for autonomous systems. However, traditional SLAM methods, which assume a static environment, often suffer from significant localization drift…

Robotics · Computer Science 2025-07-30 Haolan Zhang , Thanh Nguyen Canh , Chenghao Li , Nak Young Chong

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial…

Visual Simultaneous Localization and Mapping (vSLAM) is a widely used technique in robotics and computer vision that enables a robot to create a map of an unfamiliar environment using a camera sensor while simultaneously tracking its…

Computer Vision and Pattern Recognition · Computer Science 2023-05-10 Yasaman Haghighi , Suryansh Kumar , Jean-Philippe Thiran , Luc Van Gool

Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the environment represented by a (3D) map and the concurrent pose estimation, has made astonishing progress. Meanwhile, large scale applications aiming at the data…

Robotics · Computer Science 2025-08-06 Vincent Ress , Wei Zhang , David Skuddis , Norbert Haala , Uwe Soergel

Simultaneous localization and mapping (SLAM) are essential in numerous robotics applications, such as autonomous navigation. Traditional SLAM approaches infer the metric state of the robot along with a metric map of the environment. While…

Robotics · Computer Science 2023-02-20 Roee Mor , Vadim Indelman

Visible light positioning (VLP) technology is a promising technique as it can provide high accuracy positioning based on the existing lighting infrastructure. However, existing approaches often require dense lighting distributions.…

Robotics · Computer Science 2021-09-06 Weipeng Guan , Patrick Yue

This paper presents a novel approach to visual simultaneous localization and mapping (SLAM) using multiple RGB-D cameras. The proposed method, Multicam-SLAM, significantly enhances the robustness and accuracy of SLAM systems by capturing…

Robotics · Computer Science 2024-06-25 Shenghao Li , Luchao Pang , Xianglong Hu

This paper surveys the state-of-the-art in the nexus of SLAM and Wireless Communications, attributing the bidirectional impact of each with a focus on visual SLAM (V-SLAM) integration. We provide an overview of key concepts related to…

The integration of neural rendering and the SLAM system recently showed promising results in joint localization and photorealistic view reconstruction. However, existing methods, fully relying on implicit representations, are so…

Computer Vision and Pattern Recognition · Computer Science 2024-04-09 Huajian Huang , Longwei Li , Hui Cheng , Sai-Kit Yeung

Visual slam technology is one of the key technologies for robot to explore unknown environment independently. Accurate estimation of camera pose based on visual sensor is the basis of autonomous navigation and positioning. However, most…

Robotics · Computer Science 2020-12-01 Deng Su , Dehong Chong

Traditional Visual Simultaneous Localization and Mapping (VSLAM) systems assume a static environment, which makes them ineffective in highly dynamic settings. To overcome this, many approaches integrate semantic information from deep…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Sanghyoup Gu , Ratnesh Kumar

Motivated by the tremendous progress we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With…

Robotics · Computer Science 2022-08-03 Pierre-Yves Lajoie , Benjamin Ramtoula , Fang Wu , Giovanni Beltrame