Papers Simultaneous Localization and Mapping
“Simultaneous Localization and Mapping” 태그가 달린 논문 572편 · 필터 해제
Map completion from partial observation using the global structure of multiple environmental maps
Using the spatial structure of various indoor environments as prior knowledge, the robot would construct the map more efficiently. Autonomous mobile robots generally apply simultaneous localization and mapping (SLAM) met…
Simultaneous Localization and MappingGRIHA: Synthesizing 2-Dimensional Building Layouts from Images Captured using a Smart Phone
Reconstructing an indoor scene and generating a layout/floor plan in 3D or 2D is a widely known problem. Quite a few algorithms have been proposed in the literature recently. However, most existing methods either use RGB…
Layout GenerationSimultaneous Localization and MappingHippocampal formation-inspired probabilistic generative model
In building artificial intelligence (AI) agents, referring to how brains function in real environments can accelerate development by reducing the design space. In this study, we propose a probabilistic generative model (…
HippocampusmodelSimultaneous Localization and MappingMultiview Sensing With Unknown Permutations: An Optimal Transport Approach
In several applications, including imaging of deformable objects while in motion, simultaneous localization and mapping, and unlabeled sensing, we encounter the problem of recovering a signal that is measured subject to …
Simultaneous Localization and MappingReal-time Nonrigid Mosaicking of Laparoscopy Images
The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant th…
CPUGPUSimultaneous Localization and MappingAdvances in Inference and Representation for Simultaneous Localization and Mapping
Simultaneous localization and mapping (SLAM) is the process of constructing a global model of an environment from local observations of it; this is a foundational capability for mobile robots, supporting such core functi…
Simultaneous Localization and MappingLCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM
Loop closure detection is an essential component of Simultaneous Localization and Mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed…
Autonomous DrivingLoop Closure DetectionPoint Cloud RegistrationSimultaneous Localization and MappingSequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition
Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade. However, precision-recall performance of these …
Autonomous DrivingAutonomous NavigationAutonomous VehiclesImage Retrieval+13Panoramic annular SLAM with loop closure and global optimization
In this paper, we propose panoramic annular simultaneous localization and mapping (PA-SLAM), a visual SLAM system based on panoramic annular lens. A hybrid point selection strategy is put forward in the tracking front-en…
global-optimizationLoop Closure DetectionSimultaneous Localization and MappingA Geometric Nonlinear Stochastic Filter for Simultaneous Localization and Mapping
Simultaneous Localization and Mapping (SLAM) is one of the key robotics tasks as it tackles simultaneous mapping of the unknown environment defined by multiple landmark positions and localization of the unknown pose (i.e…
PositionSimultaneous Localization and MappingDARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments
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 exploratio…
Loop Closure DetectionSimultaneous Localization and MappingLearned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching
Successful visual navigation depends upon capturing images that contain sufficient useful information. In this letter, we explore a data-driven approach to account for environmental lighting changes, improving the qualit…
Simultaneous Localization and MappingVisual NavigationVisual OdometryOV$^{2}$SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications
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 descri…
Autonomous DrivingMonocular Visual OdometrySimultaneous Localization and MappingVisual Localization+1MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square
The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MU…
Autonomous DrivingSimultaneous Localization and MappingA Collaborative Visual SLAM Framework for Service Robots
We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots. With an edge server maintaining a map database and performing global optimization, each robot can register to a…
global-optimizationRetrievalSimultaneous Localization and MappingAutonomous Navigation in Dynamic Environments: Deep Learning-Based Approach
Mobile robotics is a research area that has witnessed incredible advances for the last decades. Robot navigation is an essential task for mobile robots. Many methods are proposed for allowing robots to navigate within di…
Autonomous NavigationDeep LearningNavigateRobot Navigation+1Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot
Visual perception of the objects in a 3D environment is a key to successful performance in autonomous driving and simultaneous localization and mapping (SLAM). In this paper, we present a real time approach for estimatin…
Autonomous DrivingObjectobject-detectionObject Detection+2A Hybrid Learner for Simultaneous Localization and Mapping
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…
Autonomous NavigationAutonomous VehiclesSelf-Driving CarsSimultaneous Localization and MappingCholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80
Computer-assisted surgery has been developed to enhance surgery correctness and safety. However, researchers and engineers suffer from limited annotated data to develop and train better algorithms. Consequently, the deve…
Semantic SegmentationSimultaneous Localization and MappingGEM: Online Globally consistent dense elevation mapping for unstructured terrain
Online dense mapping gives a representation of the unstructured terrain, which is indispensable for safe robotic motion planning. In this article, we propose such an elevation mapping system, namely GEM, to generate a de…
CPUGPUMotion PlanningSimultaneous Localization and Mapping