Papers Simultaneous Localization and Mapping
“Simultaneous Localization and Mapping” 태그가 달린 논문 572편 · 필터 해제
BEyond observation: an approach for ObjectNav
With the rise of automation, unmanned vehicles became a hot topic both as commercial products and as a scientific research topic. It composes a multi-disciplinary field of robotics that encompasses embedded systems, cont…
Autonomous NavigationObjectSimultaneous Localization and MappingTowards bio-inspired unsupervised representation learning for indoor aerial navigation
Aerial navigation in GPS-denied, indoor environments, is still an open challenge. Drones can perceive the environment from a richer set of viewpoints, while having more stringent compute and energy constraints than other…
Drone navigationRepresentation LearningSensitivitySimultaneous Localization and MappingMAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment
Running numerous experiments in simulation is a necessary step before deploying a control system on a real robot. In this paper we introduce a novel benchmark that is aimed at quantitatively evaluating the quality of vis…
Simultaneous Localization and MappingEmbedded Vision for Self-Driving on Forest Roads
Forest roads in Romania are unique natural wildlife sites used for recreation by countless tourists. In order to protect and maintain these roads, we propose RovisLab AMTU (Autonomous Mobile Test Unit), which is a roboti…
GPUInstance SegmentationNavigateSemantic Segmentation+1Unsupervised Scale-consistent Depth Learning from Video
We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time. Our contributions include: (i) we propose a geometry cons…
Depth EstimationMonocular Depth EstimationMonocular Visual OdometrySimultaneous Localization and MappingDifferentiable SLAM-net: Learning Particle SLAM for Visual Navigation
Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce …
Robot NavigationSimultaneous Localization and MappingVisual NavigationLatentSLAM: unsupervised multi-sensor representation learning for localization and mapping
Biologically inspired algorithms for simultaneous localization and mapping (SLAM) such as RatSLAM have been shown to yield effective and robust robot navigation in both indoor and outdoor environments. One drawback howev…
Representation LearningRobot NavigationSimultaneous Localization and MappingTemplate MatchingReal-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization
Simultaneous Localization and Mapping (SLAM) is an essential capability for autonomous robots, but due to high data rates of 3D LiDARs real-time SLAM is challenging. We propose a real-time method for 6D LiDAR odometry. O…
Simultaneous Localization and MappingSVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition
Point cloud-based large scale place recognition is fundamental for many applications like Simultaneous Localization and Mapping (SLAM). Although many models have been proposed and have achieved good performance by learni…
3D Place RecognitionPoint Cloud RetrievalSimultaneous Localization and MappingImproved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames
Monocular simultaneous localization and mapping (SLAM) is emerging in advanced driver assistance systems and autonomous driving, because a single camera is cheap and easy to install. Conventional monocular SLAM has two m…
Autonomous DrivingCPUGPUSegmentation+2Fisheye Lens Camera based Autonomous Valet Parking System
This paper proposes an efficient autonomous valet parking system utilizing only cameras which are the most widely used sensor. To capture more information instantaneously and respond rapidly to changes in the surrounding…
Simultaneous Localization and MappingSelf-optimizing loop sifting and majorization for 3D reconstruction
Visual simultaneous localization and mapping (vSLAM) and 3D reconstruction methods have gone through impressive progress. These methods are very promising for autonomous vehicle and consumer robot applications because th…
3D ReconstructionSimultaneous Localization and MappingReal-time dense 3D Reconstruction from monocular video data captured by low-cost UAVs
Real-time 3D reconstruction enables fast dense mapping of the environment which benefits numerous applications, such as navigation or live evaluation of an emergency. In contrast to most real-time capable approaches, our…
3D ReconstructionSimultaneous Localization and MappingGraph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment. However, the performance of such systems highly depends on the ambient illumination condit…
feature selectionProbabilistic Deep LearningSimultaneous Localization and MappingA Front-End for Dense Monocular SLAM using a Learned Outlier Mask Prior
Recent achievements in depth prediction from a single RGB image have powered the new research area of combining convolutional neural networks (CNNs) with classical simultaneous localization and mapping (SLAM) algorithms.…
Depth EstimationDepth PredictionPredictionSimultaneous Localization and MappingA Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis
Seven million people suffer surgical complications each year, but with sufficient surgical training and review, 50\% of these complications could be prevented. To improve surgical performance, existing research uses vari…
image-classificationImage Classificationobject-detectionObject Detection+3LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method
The Simultaneous Localization and Mapping (SLAM) problem addresses the possibility of a robot to localize itself in an unknown environment and simultaneously build a consistent map of this environment. Recently, cameras …
Deep LearningSimultaneous Localization and MappingTransfer LearningA comparative evaluation of learned feature descriptors on hybrid monocular visual SLAM methods
Classical Visual Simultaneous Localization and Mapping (VSLAM) algorithms can be easily induced to fail when either the robot's motion or the environment is too challenging. The use of Deep Neural Networks to enhance VSL…
Simultaneous Localization and MappingSD-6DoF-ICLK: Sparse and Deep Inverse Compositional Lucas-Kanade Algorithm on SE(3)
This paper introduces SD-6DoF-ICLK, a learning-based Inverse Compositional Lucas-Kanade (ICLK) pipeline that uses sparse depth information to optimize the relative pose that best aligns two images on SE(3). To compute th…
Simultaneous Localization and MappingMultipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models
In this paper, we present a Bayesian multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts interacting multiple models (IMM) parameters to describe the mobile agent state dynamic…
Simultaneous Localization and Mapping