Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey [PDF]
Sensors, 2021 Loop closure detection is of vital importance in the process of simultaneous localization and mapping (SLAM), as it helps to reduce the cumulative error of the robot’s estimated pose and generate a consistent global map.
Saba Arshad, Gon-Woo Kim
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A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization
Remote Sensing, 2021 LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules.
Shoubin Chen +4 more
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Loop Closure Detection Using Local 3D Deep Descriptors [PDF]
IEEE Robotics and Automation Letters, 2022 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 from point clouds that are learnt from data using a deep learning algorithm.
Youjie Zhou +4 more
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Approach to Semantic Visual SLAM for Bionic Robots Based on Loop Closure Detection with Combinatorial Graph Entropy in Complex Dynamic Scenes [PDF]
BiomimeticsIn complex dynamic environments, the performance of SLAM systems on bionic robots is susceptible to interference from dynamic objects or structural changes in the environment.
Dazheng Wang, Jingwen Luo
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Accurate localization of indoor high similarity scenes using visual slam combined with loop closure detection algorithm. [PDF]
PLoS OneAccurate localization is a critical technology for the application of intelligent robots and automation systems in complex indoor environments. Traditional visual SLAM (Simultaneous Localization and Mapping) techniques often face challenges with ...
Xiang Z +8 more
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The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection [PDF]
IEEE Transactions on Intelligent Transportation Systems, 2022 Where am I? This is one of the most critical questions that any intelligent system should answer to decide whether it navigates to a previously visited area.
Konstantinos A. Tsintotas +2 more
semanticscholar +3 more sources
Loop Closure Detection with CNN in RGB-D SLAM for Intelligent Agricultural Equipment
AgricultureLoop closure detection plays an important role in the construction of reliable maps for intelligent agricultural machinery equipment. With the combination of convolutional neural networks (CNN), its accuracy and real-time performance are better than ...
Haixia Qi +3 more
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Loop Closure Detection Via Maximization of Mutual Information [PDF]
IEEE Access, 2019 An image can be described in terms of appearance frequency of visual words. This representation is implemented in bag-of-visual-words (BoVW)-based loop closure detection for its efficiency and effectiveness. However, traditional BoVW-based approaches are
Ge Zhang, Xiaoqiang Yan, Yangdong Ye
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A Robust and Lightweight Loop Closure Detection Approach for Challenging Environments
DronesLoop closure detection is crucial for simultaneous localization and mapping (SLAM), as it can effectively correct the accumulated errors. Complex scenarios put forward high requirements on the robustness of loop closure detection.
Yuan Shi +3 more
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LiDAR-IMU Tightly-Coupled SLAM Method Based on IEKF and Loop Closure Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingSimultaneous Localization and Mapping (SLAM) technology based on LiDAR can achieve real-time robot positioning and establish environmental maps in unknown environments. LiDAR odometry can achieve accurate pose estimation in short distances or small-scale
Huimin Pan +4 more
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