Papers Simultaneous Localization and Mapping
“Simultaneous Localization and Mapping” 태그가 달린 논문 572편 · 필터 해제
Det-SLAM: A semantic visual SLAM for highly dynamic scenes using Detectron2
According to experts, Simultaneous Localization and Mapping (SLAM) is an intrinsic part of autonomous robotic systems. Several SLAM systems with impressive performance have been invented and used during the last several …
Semantic SegmentationSemantic SLAMSimultaneous Localization and MappingClosing the Loop: Graph Networks to Unify Semantic Objects and Visual Features for Multi-object Scenes
In Simultaneous Localization and Mapping (SLAM), Loop Closure Detection (LCD) is essential to minimize drift when recognizing previously visited places. Visual Bag-of-Words (vBoW) has been an LCD algorithm of choice for …
Graph MatchingLoop Closure DetectionSimultaneous Localization and MappingLMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDAR SLAM
LiDAR odometry is one of the essential parts of LiDAR simultaneous localization and mapping (SLAM). However, existing LiDAR odometry tends to match a new scan simply iteratively with previous fixed-pose scans, gradually …
Simultaneous Localization and MappingOA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM
In this work, we explore the use of objects in Simultaneous Localization and Mapping in unseen worlds and propose an object-aided system (OA-SLAM). More precisely, we show that, compared to low-level points, the major be…
Camera RelocalizationObjectPose TrackingSimultaneous Localization and MappingSemantic Visual Simultaneous Localization and Mapping: A Survey
Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such as autonomous robot navigation and AR/V…
Robot NavigationSimultaneous Localization and MappingSurveyOptimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets
Simultaneous Localization and Mapping (SLAM) is considered an ever-evolving problem due to its usage in many applications. Evaluation of SLAM is done typically using publicly available datasets which are increasing in nu…
Simultaneous Localization and MappingLF-VISLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents
Simultaneous Localization And Mapping (SLAM) has become a crucial aspect in the fields of autonomous driving and robotics. One crucial component of visual SLAM is the Field-of-View (FoV) of the camera, as a larger FoV al…
Autonomous DrivingSimultaneous Localization and MappingGeneral Place Recognition Survey: Towards the Real-world Autonomy Age
Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made as…
Loop Closure DetectionSimultaneous Localization and MappingSurveyR$^3$LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator
Simultaneous localization and mapping (SLAM) are crucial for autonomous robots (e.g., self-driving cars, autonomous drones), 3D mapping systems, and AR/VR applications. This work proposed a novel LiDAR-inertial-visual fu…
Self-Driving CarsSimultaneous Localization and MappingState EstimationVisual Odometry with Neuromorphic Resonator Networks
Visual Odometry (VO) is a method to estimate self-motion of a mobile robot using visual sensors. Unlike odometry based on integrating differential measurements that can accumulate errors, such as inertial sensors or whee…
Motion EstimationNavigateSimultaneous Localization and MappingVisual OdometryDoppler Exploitation in Bistatic mmWave Radio SLAM
Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environ…
Simultaneous Localization and MappingBoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM
Loop closing is a fundamental part of simultaneous localization and mapping (SLAM) for autonomous mobile systems. In the field of visual SLAM, bag of words (BoW) has achieved great success in loop closure. The BoW featur…
4kSimultaneous Localization and MappingEvaluation and comparison of eight popular Lidar and Visual SLAM algorithms
In this paper, we evaluate eight popular and open-source 3D Lidar and visual SLAM (Simultaneous Localization and Mapping) algorithms, namely LOAM, Lego LOAM, LIO SAM, HDL Graph, ORB SLAM3, Basalt VIO, and SVO2. We have d…
Simultaneous Localization and MappingPLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes
In this paper, we consider the problems in the practical application of visual simultaneous localization and mapping (SLAM). With the popularization and application of the technology in wide scope, the practicability of …
Loop Closure DetectionPose EstimationSimultaneous Localization and MappingD$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance
In this paper, we introduce a self-supervised deep SLAM method that robustly operates in dynamic scenes while accurately identifying dynamic components. Our method leverages a dual-flow representation for static flow and…
Pose EstimationSimultaneous Localization and MappingORB-based SLAM accelerator on SoC FPGA
Simultaneous Localization and Mapping (SLAM) is one of the main components of autonomous navigation systems. With the increase in popularity of drones, autonomous navigation on low-power systems is seeing widespread appl…
Autonomous NavigationCPUSimultaneous Localization and MappingVI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization
Fingerprinting-based approaches are particularly suitable for deploying indoor positioning systems for pedestrians with minimal infrastructure costs. The accuracy of the method, however, strongly depends on the quality o…
Indoor LocalizationSimultaneous Localization and MappingVECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM
Event cameras have recently gained in popularity as they hold strong potential to complement regular cameras in situations of high dynamics or challenging illumination. An important problem that may benefit from the addi…
Simultaneous Localization and MappingWireless Channel Prediction in Partially Observed Environments
Site-specific radio frequency (RF) propagation prediction increasingly relies on models built from visual data such as cameras and LIDAR sensors. When operating in dynamic settings, the environment may only be partially …
PredictionSimultaneous Localization and MappingAutomated Wheat Disease Detection using a ROS-based Autonomous Guided UAV
With the increase in world population, food resources have to be modified to be more productive, resistive, and reliable. Wheat is one of the most important food resources in the world, mainly because of the variety of w…
Simultaneous Localization and Mapping