相关论文: 3D Active Metric-Semantic SLAM
Accurate and robust localization and mapping are essential components for most autonomous robots. In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact…
Multi-robot SLAM systems in GPS-denied environments require loop closures to maintain a drift-free centralized map. With an increasing number of robots and size of the environment, checking and computing the transformation for all the loop…
Accurate reconstruction of the environment is a central goal of Simultaneous Localization and Mapping (SLAM) systems. However, the agent's trajectory can significantly affect estimation accuracy. This paper presents a new method to model…
Robustness in Simultaneous Localization and Mapping (SLAM) remains one of the key challenges for the real-world deployment of autonomous systems. SLAM research has seen significant progress in the last two and a half decades, yet many…
Semantic SLAM (Simultaneous Localization and Mapping) systems enrich robot maps with structural and semantic information, enabling robots to operate more effectively in complex environments. However, these systems struggle in real-world…
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…
Simultaneous localization and mapping (SLAM) provides user tracking and environmental mapping capabilities, enabling communication systems to gain situational awareness. Advanced communication networks with ultra-wideband, multiple…
Geometric navigation is nowadays a well-established field of robotics and the research focus is shifting towards higher-level scene understanding, such as Semantic Mapping. When a robot needs to interact with its environment, it must be…
Inter-robot loop closure detection, e.g., for collaborative simultaneous localization and mapping (CSLAM), is a fundamental capability for many multirobot applications in GPS-denied regimes. In real-world scenarios, this is a…
Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while simultaneously localising itself with respect to the map.…
Recent progress in object pose prediction provides a promising path for robots to build object-level scene representations during navigation. However, as we deploy a robot in novel environments, the out-of-distribution data can degrade the…
An essential task for a multi-robot system is generating a common understanding of the environment and relative poses between robots. Cooperative tasks can be executed only when a vehicle has knowledge of its own state and the states of the…
This paper proposes a novel active Simultaneous Localization and Mapping (SLAM) method with continuous trajectory optimization over a stochastic robot dynamics model. The problem is formalized as a stochastic optimal control over the…
The bundle of geometry and appearance in computer vision has proven to be a promising solution for robots across a wide variety of applications. Stereo cameras and RGB-D sensors are widely used to realise fast 3D reconstruction and…
Multi-agent cooperative SLAM often encounters challenges in similar indoor environments characterized by repetitive structures, such as corridors and rooms. These challenges can lead to significant inaccuracies in shared location…
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) 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…
Enabling fully autonomous robots capable of navigating and exploring large-scale, unknown and complex environments has been at the core of robotics research for several decades. A key requirement in autonomous exploration is building…
Agricultural robots must navigate challenging dynamic and semi-structured environments. Recently, environmental modeling using LiDAR-based SLAM has shown promise in providing highly accurate geometry. However, how this chaotic environmental…
In this paper, we present a multi-agent framework for real-time large-scale 3D reconstruction applications. In SLAM, researchers usually build and update a 3D map after applying non-linear pose graph optimization techniques. Moreover, many…