Papers Loop Closure Detection
“Loop Closure Detection” 태그가 달린 논문 89편 · 필터 해제
DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition
LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that capture…
Graph Neural NetworkLoop Closure DetectionAverage Outward Flux Skeletons for Environment Mapping and Topology Matching
We consider how to directly extract a road map (also known as a topological representation) of an initially-unknown 2-dimensional environment via an online procedure that robustly computes a retraction of its boundaries.…
Loop Closure DetectionLoop closure detection using local 3D deep descriptors
We present a simple yet effective method to address loop closure detection in simultaneous localisation and mapping using local 3D deep descriptors (L3Ds). L3Ds are emerging compact representations of patches extracted f…
Loop Closure DetectionSymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words
Loop closure detection is an essential tool of Simultaneous Localization and Mapping (SLAM) to minimize drift in its localization. Many state-of-the-art loop closure detection (LCD) algorithms use visual Bag-of-Words (vB…
Loop Closure DetectionSimultaneous Localization and MappingCT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure
Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous cars for localization and perception tasks, both relying on the ability to build a precise map of the environment. For this, we pro…
CPULoop Closure DetectionRTEAirLoop: Lifelong Loop Closure Detection
Loop closure detection is an important building block that ensures the accuracy and robustness of simultaneous localization and mapping (SLAM) systems. Due to their generalization ability, CNN-based approaches have recei…
Incremental LearningLifelong learningLoop Closure DetectionSimultaneous Localization and MappingOn the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM
We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments. A new architecture of the deep neural network is presented …
DescriptiveLoop Closure DetectionAutomatic Vocabulary and Graph Verification for Accurate Loop Closure Detection
Localizing pre-visited places during long-term simultaneous localization and mapping, i.e. loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies. As one of the most effective and eff…
Loop Closure DetectionSimultaneous Localization and MappingProbabilistic Appearance-Invariant Topometric Localization with New Place Awareness
Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data. Recently, probabil…
Loop Closure DetectionState EstimationVisual Place RecognitionSA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure
LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue. With the development of 3D semantic segmentation for point cloud, semantic information …
3D Semantic SegmentationLoop Closure DetectionSemantic SegmentationAttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition
LiDAR-based place recognition is one of the key components of SLAM and global localization in autonomous vehicles and robotics applications. With the success of DL approaches in learning useful information from 3D LiDARs…
Autonomous VehiclesLoop Closure DetectionNDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in lidar-based SLAM systems. This paper pr…
Autonomous DrivingLoop Closure DetectionPoint Cloud RetrievalRetrievalBeyond ANN: Exploiting Structural Knowledge for Efficient Place Recognition
Visual place recognition is the task of recognizing same places of query images in a set of database images, despite potential condition changes due to time of day, weather or seasons. It is important for loop closure de…
Loop Closure DetectionVisual Place RecognitionLCDNet: 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 MappingPanoramic 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 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 MappingAccurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry
Scale ambiguity is a fundamental problem in monocular visual odometry. Typical solutions include loop closure detection and environment information mining. For applications like self-driving cars, loop closure is not alw…
Loop Closure DetectionMonocular Visual OdometrySelf-Driving CarsTranslation+1Visual place recognition: A survey from deep learning perspective
Visual place recognition has attracted widespread research interest in multiple fields such as computer vision and robotics. Recently, researchers have employed advanced deep learning techniques to tackle this problem. W…
Deep LearningLoop Closure DetectionScene UnderstandingSimultaneous Localization and Mapping+3Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs
In recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications.This article proposes an appearance-ba…
Loop Closure DetectionSimultaneous Localization and MappingPlace Recognition in Forests with Urquhart Tessellations
In this letter, we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest. We propose a framework that uses these descriptors to detect previously seen observations and …
Loop Closure DetectionPosition