Papers Simultaneous Localization and Mapping
“Simultaneous Localization and Mapping” 태그가 달린 논문 572편 · 필터 해제
Structure Aware SLAM using Quadrics and Planes
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information. On the other ha…
Camera LocalizationObjectobject-detectionObject Detection+1Closing the Calibration Loop: An Inside-out-tracking Paradigm for Augmented Reality in Orthopedic Surgery
In percutaneous orthopedic interventions the surgeon attempts to reduce and fixate fractures in bony structures. The complexity of these interventions arises when the surgeon performs the challenging task of navigating s…
AnatomySimultaneous Localization and MappingVision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction
We present an unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner),…
Simultaneous Localization and MappingVisual OdometryGlobal Pose Estimation with an Attention-based Recurrent Network
The ability for an agent to localize itself within an environment is crucial for many real-world applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) enables incremental and concurrent bui…
Feature EngineeringPose EstimationSimultaneous Localization and MappingVisual OdometryUnsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video. Unsupervised learning removes the need for separate supervisory signals (depth or ego-motion ground truth, or multi-view…
3D geometryDepth And Camera MotionDepth EstimationMonocular Depth Estimation+1LoopSmart: Smart Visual SLAM Through Surface Loop Closure
We present a visual simultaneous localization and mapping (SLAM) framework of closing surface loops. It combines both sparse feature matching and dense surface alignment. Sparse feature matching is used for visual odomet…
Point Cloud RegistrationSimultaneous Localization and MappingSurface ReconstructionVisual OdometryOn-the-fly Augmented Reality for Orthopaedic Surgery Using a Multi-Modal Fiducial
Fluoroscopic X-ray guidance is a cornerstone for percutaneous orthopaedic surgical procedures. However, two-dimensional observations of the three-dimensional anatomy suffer from the effects of projective simplification. …
AnatomySimultaneous Localization and MappingEgocentric Spatial Memory Network
Inspired by neurophysiological discoveries of navigation cells in the mammalian brain, we introduce the first deep neural network architecture for modeling Egocentric Spatial Memory (ESM). It learns to estimate the pose …
NavigateSimultaneous Localization and MappingReal-time 3D Reconstruction on Construction Site using Visual SLAM and UAV
3D reconstruction can be used as a platform to monitor the performance of activities on construction site, such as construction progress monitoring, structure inspection and post-disaster rescue. Comparing to other senso…
3D ReconstructionSimultaneous Localization and MappingLearning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction
Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images. These features, while suitable for sparse mappi…
Depth EstimationMonocular ReconstructionSimultaneous Localization and MappingBacktracking Regression Forests for Accurate Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection. Recent random forests based methods directly p…
Camera RelocalizationLoop Closure DetectionregressionSimultaneous Localization and MappingPIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design
In this paper, we present the PerceptIn Robotics Vision System (PIRVS) system, a visual-inertial computing hardware with embedded simultaneous localization and mapping (SLAM) algorithm. The PIRVS hardware is equipped wit…
Sensor FusionSimultaneous Localization and MappingSpace-Time Localization and Mapping
This paper addresses the problem of building a spatio-temporal model of the world from a stream of time-stamped data. Unlike traditional models for simultaneous localization and mapping (SLAM) and structure-from-motion (…
Simultaneous Localization and MappingSparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, to attain a hig…
Depth EstimationDepth PredictionPredictionSimultaneous Localization and Mapping+1Direction-Aware Semi-Dense SLAM
To aide simultaneous localization and mapping (SLAM), future perception systems will incorporate forms of scene understanding. In a step towards fully integrated probabilistic geometric scene understanding, localization …
Scene UnderstandingSegmentationSimultaneous Localization and MappingImproving Sonar Image Patch Matching via Deep Learning
Matching sonar images with high accuracy has been a problem for a long time, as sonar images are inherently hard to model due to reflections, noise and viewpoint dependence. Autonomous Underwater Vehicles require good so…
Binary ClassificationDeep Learningobject-detectionObject Detection+2CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation
This paper presents a novel beacon light coding protocol, which enables fast and accurate identification of the beacons in an image. The protocol is provably robust to a predefined set of detection and decoding errors, a…
Object TrackingSimultaneous Localization and MappingIncremental 3D Line Segment Extraction from Semi-dense SLAM
Although semi-dense Simultaneous Localization and Mapping (SLAM) has been becoming more popular over the last few years, there is a lack of efficient methods for representing and processing their large scale point clouds…
ClusteringSimultaneous Localization and MappingSurface ReconstructionA Solution for Crime Scene Reconstruction using Time-of-Flight Cameras
In this work, we propose a method for three-dimensional (3D) reconstruction of wide crime scene, based on a Simultaneous Localization and Mapping (SLAM) approach. We used a Kinect V2 Time-of-Flight (TOF) RGB-D camera to …
3D ReconstructionSimultaneous Localization and MappingInfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure
Volumetric models have become a popular representation for 3D scenes in recent years. One breakthrough leading to their popularity was KinectFusion, which focuses on 3D reconstruction using RGB-D sensors. However, monocu…
3D ReconstructionGPUSimultaneous Localization and Mapping