Papers Simultaneous Localization and Mapping
“Simultaneous Localization and Mapping” 태그가 달린 논문 572편 · 필터 해제
Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping
Efficient object level representation for monocular semantic simultaneous localization and mapping (SLAM) still lacks a widely accepted solution. In this paper, we propose the use of an efficient representation, based on…
ObjectPositionSemantic SLAMSimultaneous Localization and MappingData Fusion for Radio Frequency SLAM with Robust Sampling
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 ancho…
Indoor LocalizationSimultaneous Localization and MappingATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping
In this paper, a novel solution is introduced for visual Simultaneous Localization and Mapping (vSLAM) that is built up of Deep Learning components. The proposed architecture is a highly modular framework in which each c…
AllAutonomous DrivingDeep LearningSimultaneous Localization and Mapping+1ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy
The Iterative Closest Point (ICP) algorithm is one of the most important algorithms for geometric alignment of three-dimensional surface registration, which is frequently used in computer vision tasks, including the Simu…
Simultaneous Localization and MappingExtracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding
Semantic 3D scene understanding is a problem of critical importance in robotics. While significant advances have been made in simultaneous localization and mapping algorithms, robots are still far from having the common …
Common Sense ReasoningScene UnderstandingSimultaneous Localization and MappingS3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization
Camera relocalization is the key component of simultaneous localization and mapping (SLAM) systems. This paper proposes a learning-based approach, named Sparse Spatial Scene Embedding with Graph Neural Networks (S3E-GNN)…
Camera RelocalizationSimultaneous Localization and MappingPMBM-based SLAM Filters in 5G mmWave Vehicular Networks
Radio-based vehicular simultaneous localization and mapping (SLAM) aims to localize vehicles while mapping the landmarks in the environment. We propose a sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLA…
Simultaneous Localization and MappingEndoMapper dataset of complete calibrated endoscopy procedures
Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on the automatic detection of polyps or other pathologies, but localization and navigation of the endoscope are complet…
Medical Image RetrievalSimultaneous Localization and MappingStruct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities from Visual SLAM
Feature-based visual simultaneous localization and mapping (SLAM) methods only estimate the depth of extracted features, generating a sparse depth map. To solve this sparsity problem, depth completion tasks that estimate…
Depth CompletionSimultaneous Localization and MappingIndoor simultaneous localization and mapping based on fringe projection profilometry
Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics. Outdoor SLAM has been widely used with the assistance of LiDAR o…
Autonomous DrivingSimultaneous Localization and MappingSparse Image based Navigation Architecture to Mitigate the need of precise Localization in Mobile Robots
Traditional simultaneous localization and mapping (SLAM) methods focus on improvement in the robot's localization under environment and sensor uncertainty. This paper, however, focuses on mitigating the need for exact lo…
Autonomous NavigationNavigateSimultaneous Localization and MappingIndoor SLAM Using a Foot-mounted IMU and the local Magnetic Field
In this paper, a simultaneous localization and mapping (SLAM) algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit (IMU) is proposed. The algorithm uses two maps, namely, a moti…
PositionSimultaneous Localization and MappingSelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
Monocular depth estimation in the wild inherently predicts depth up to an unknown scale. To resolve scale ambiguity issue, we present a learning algorithm that leverages monocular simultaneous localization and mapping (S…
Depth EstimationMonocular Depth EstimationRobot NavigationSelf-Supervised Learning+1An Online Semantic Mapping System for Extending and Enhancing Visual SLAM
We present a real-time semantic mapping approach for mobile vision systems with a 2D to 3D object detection pipeline and rapid data association for generated landmarks. Besides the semantic map enrichment the associated …
3D Object DetectionObjectobject-detectionObject Detection+3Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition
Visual place recognition techniques based on deep learning, which have imposed themselves as the state-of-the-art in recent years, do not generalize well to environments visually different from the training set. Thus, to…
Simultaneous Localization and MappingVisual Place RecognitionSTUN: Self-Teaching Uncertainty Estimation for Place Recognition
Place recognition is key to Simultaneous Localization and Mapping (SLAM) and spatial perception. However, a place recognition in the wild often suffers from erroneous predictions due to image variations, e.g., changing v…
Metric LearningSimultaneous Localization and MappingDescriptellation: Deep Learned Constellation Descriptors
Current descriptors for global localization often struggle under vast viewpoint or appearance changes. One possible improvement is the addition of topological information on semantic objects. However, handcrafted topolog…
Simultaneous Localization and MappingTwistSLAM: Constrained SLAM in Dynamic Environment
Classical visual simultaneous localization and mapping (SLAM) algorithms usually assume the environment to be rigid. This assumption limits the applicability of those algorithms as they are unable to accurately estimate …
Object TrackingPose EstimationSimultaneous Localization and MappingDL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization
Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM…
Autonomous DrivingObjectobject-detectionObject Detection+4SAGE: SLAM with Appearance and Geometry Prior for Endoscopy
In endoscopy, many applications (e.g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry of the observed anatomy from a monocular…
3D geometryAnatomySimultaneous Localization and Mapping