Results 11 to 20 of about 1,468,959 (299)
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
doaj +2 more sources
A Robust and Efficient Loop Closure Detection Approach for Hybrid Ground/Aerial Vehicles
Drones, 2023 Frequent and dramatic viewpoint changes make loop closure detection of hybrid ground/aerial vehicles extremely challenging. To address this issue, we present a robust and efficient loop closure detection approach based on the state-of-the-art ...
Yutong Wang +3 more
doaj +2 more sources
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
openaire +5 more sources
LiDAR Iris for Loop-Closure Detection [PDF]
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation.
Wang, Ying +5 more
openaire +5 more sources
SemanticLoop: Loop Closure With 3D Semantic Graph Matching [PDF]
IEEE Robotics and Automation Letters, 2023 Loop closure can effectively correct the accumulated error in robot localization, which plays a critical role in the long-term navigation of the robot. Traditional appearance-based methods rely on local features and are prone to failure in ambiguous environments. On the other hand, object recognition can infer objects' category, pose, and extent. These
Junfeng Yu, Shaojie Shen
openaire +4 more sources
AirLoop: Lifelong Loop Closure Detection [PDF]
2022 International Conference on Robotics and Automation (ICRA), 2022 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 received increasing attention. Although they normally benefit from training on datasets that are diverse and reflective of the environments,
Gao, Dasong +2 more
openaire +3 more sources
SVG-Loop: Semantic–Visual–Geometric Information-Based Loop Closure Detection
Remote Sensing, 2021 Loop closure detection is an important component of visual simultaneous localization and mapping (SLAM). However, most existing loop closure detection methods are vulnerable to complex environments and use limited information from images. As higher-level
Zhian Yuan +4 more
doaj +2 more sources
Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey. [PDF]
Sensors (Basel), 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.
Arshad S, Kim GW.
europepmc +2 more sources
LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM [PDF]
IEEE Transactions on robotics, 2021 Loop closure detection is an essential component of simultaneous localization and mapping (SLAM) systems, which reduces the drift accumulated over time.
Daniele Cattaneo +2 more
semanticscholar +1 more source
The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection [PDF]
IEEE transactions on intelligent transportation systems (Print), 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 +1 more source