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Contact-rich manipulation is difficult for robots to execute and requires accurate perception of the environment. In some scenarios, vision is occluded. The robot can then no longer obtain real-time scene state information through visual…

机器人学 · 计算机科学 2026-01-27 Gaozhao Wang , Xing Liu , Zhenduo Ye , Zhengxiong Liu , Panfeng Huang

Where am I? This is one of the most critical questions that any intelligent system should answer to decide whether it navigates to a previously visited area. This problem has long been acknowledged for its challenging nature in simultaneous…

机器人学 · 计算机科学 2022-11-10 Konstantinos A. Tsintotas , Loukas Bampis , Antonios Gasteratos

Researchers and robotic development groups have recently started paying special attention to autonomous mobile robot navigation in indoor environments using vision sensors. The required data is provided for robot navigation and object…

The efficiency and accuracy of mapping are crucial in a large scene and long-term AR applications. Multi-agent cooperative SLAM is the precondition of multi-user AR interaction. The cooperation of multiple smart phones has the potential to…

机器人学 · 计算机科学 2021-06-24 Jialing Liu , Ruyu Liu , Kaiqi Chen , Jianhua Zhang , Dongyan Guo

This paper describes a stereo image-based visual servoing system for trajectory tracking by a non-holonomic robot without externally derived pose information nor a known visual map of the environment. It is called trajectory servoing. The…

机器人学 · 计算机科学 2022-06-15 Shiyu Feng , Zixuan Wu , Yipu Zhao , Patricio A. Vela

A robust and efficient Simultaneous Localization and Mapping (SLAM) system is essential for robot autonomy. For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and…

计算机视觉与模式识别 · 计算机科学 2020-08-13 Dongjiang Li , Xuesong Shi , Qiwei Long , Shenghui Liu , Wei Yang , Fangshi Wang , Qi Wei , Fei Qiao

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning…

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning…

We present a robust, real-time RGB SLAM system that handles dynamic environments by leveraging differentiable Uncertainty-aware Bundle Adjustment. Traditional SLAM methods typically assume static scenes, leading to tracking failures in the…

计算机视觉与模式识别 · 计算机科学 2026-03-20 Moyang Li , Zihan Zhu , Marc Pollefeys , Daniel Barath

To address the weak observability of monocular visual-inertial odometers on ground-based mobile robots, this paper proposes a monocular inertial SLAM algorithm combined with wheel speed anomaly detection. The algorithm uses a wheel speed…

机器人学 · 计算机科学 2020-03-24 Peng Gang , Lu Zezao , Chen Shanliang , Chen Bocheng , He Dingxin

This paper presents ORB-SLAM, a feature-based monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and…

机器人学 · 计算机科学 2015-09-21 Raul Mur-Artal , J. M. M. Montiel , Juan D. Tardos

Many applications of Visual SLAM, such as augmented reality, virtual reality, robotics or autonomous driving, require versatile, robust and precise solutions, most often with real-time capability. In this work, we describe OV$^{2}$SLAM, a…

计算机视觉与模式识别 · 计算机科学 2021-02-09 Maxime Ferrera , Alexandre Eudes , Julien Moras , Martial Sanfourche , Guy Le Besnerais

We present VIGS-SLAM, a visual-inertial 3D Gaussian Splatting SLAM system that achieves robust real-time tracking and high-fidelity reconstruction. Although recent 3DGS-based SLAM methods achieve dense and photorealistic mapping, their…

机器人学 · 计算机科学 2026-03-16 Zihan Zhu , Wei Zhang , Moyang Li , Norbert Haala , Marc Pollefeys , Daniel Barath

Simultaneous localization and mapping (SLAM) is an essential component of robotic systems. In this work we perform a feasibility study of RGB-D SLAM for the task of indoor robot navigation. Recent visual SLAM methods, e.g. ORBSLAM2…

计算机视觉与模式识别 · 计算机科学 2019-10-14 David Prokhorov , Dmitry Zhukov , Olga Barinova , Anna Vorontsova , Anton Konushin

Simultaneous Localization and Mapping (SLAM) allows mobile robots to navigate without external positioning systems or pre-existing maps. Radar is emerging as a valuable sensing tool, especially in vision-obstructed environments, as it is…

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…

机器人学 · 计算机科学 2025-07-30 Haolan Zhang , Thanh Nguyen Canh , Chenghao Li , Nak Young Chong

Exploration is an important step in autonomous navigation of robotic systems. In this paper we introduce a series of enhancements for exploration algorithms in order to use them with vision-based simultaneous localization and mapping…

机器人学 · 计算机科学 2021-10-19 Kirill Muravyev , Andrey Bokovoy , Konstantin Yakovlev

In this paper we present a data-driven approach to obtain the static image of a scene, eliminating dynamic objects that might have been present at the time of traversing the scene with a camera. The general objective is to improve…

计算机视觉与模式识别 · 计算机科学 2020-10-16 Berta Bescos , Cesar Cadena , Jose Neira

In the proposed study, we describe an approach to improving the computational efficiency and robustness of visual SLAM algorithms on mobile robots with multiple cameras and limited computational power by implementing an intermediate layer…

Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such as autonomous robot navigation and AR/VR. However, vSLAM…

计算机视觉与模式识别 · 计算机科学 2025-03-19 Kaiqi Chen , Junhao Xiao , Jialing Liu , Qiyi Tong , Heng Zhang , Ruyu Liu , Jianhua Zhang , Arash Ajoudani , Shengyong Chen