Real-time loop closure in 2D LIDAR SLAM

By Wolfgang Hess, Damon Kohler, Holger H. Rapp, D. Andor. IEEE International Conference on Robotics and Automation, 2016

1,836 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/579735c1e5b2b0ae7fb42fcb9e2433f3118afd20

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Citations in the corpus

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “[64] Cartographer [65] Lidar & IMU Octomap Modular Frontiers Map’s MI & distance Sim.”
Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments (2018, methodology): “[10], we also perform the actual pose graph optimization in a separate thread.”
F-LOAM : Fast LiDAR Odometry and Mapping (2021, background): “For example, [5] showed good performance in indoor scenario, but the localization accuracy drops a lot in outdoor environment.”
The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications (2024, positive): “A wide range of algorithms have been developed for this purpose, including the widely used scan matching algorithm [226] and approaches bor-rowed from image processing, such as those utilizing the Fourier–Mellin transform [225], [227].”
Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing (2022, methodology): “In Nobis et al. [134] an adaption and enhancement of well-known simultaneous localization and mapping (SLAM) algorithms (Google Cartographer [236], GMapping) are displayed to create a map of large-scale outdoor environments.”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, methodology): “Cartographer [121], a SLAM package from Google, improves performance by using submap and loop closure while supporting both 2-D and 3-D LiDAR.”
The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception (2018, methodology): “For outdoor sequences we rely on Cartographer to perform loop closure and fuse lidar sweeps and IMU data into a single loop-closed 2D pose of the body (lidar in this case) with minimal drift.”
PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features (2018, neutral): “Several types of sensors are utilized for localization and navigation, such as global navigation satellite systems (GNSSs) [1], laser lidar [2,3], inertial measurement units (IMUs), and cameras [4,5].”
A2D2: Audi Autonomous Driving Dataset (2020, neutral): “[19, 20, 21].”
LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation (2019, background): “[10] provided an efficient loop closure mechanism to enable large-scale mapping in real time.”
Self-Driving Cars: A Survey (2019, methodology): “There are other algorithms for online grid mapping (Hess et al., 2016; Kohlbrecher et al., 2011).”
Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation (2021, methodology): “Optimization-based approaches apply bundle adjustment on a set of keyframes and local maps; and they are popular for both visual [53; 47; 46; 38] and lidar-based SLAM [28].”
RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation (2018, methodology): “In Because of its memory leak explained in Section 3.1.1, open ‐ source lidar ‐ based SLAM approaches, that is, Google Cartographer (Hess et al., 2016), Karto SLAM (Vincent et al., 2010), Hector SLAM (Kohlbrecher et al., 2011), and GMapping (Grisetti et al., 2007).”
A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping (2020, methodology): “As a scan matcher between every scan can hardly run in real time, submaps that represent the environment of several scans were used in [59].”
Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging (2018, background): “For example, if the vision sensors cannot obtain features adequate to identify a UAV’s location, they incur a high computational cost and accumulate localization errors (Hess et al., 2016).”
A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation (2018, background): “SLAM technology spans from expensive systems based on Light Imaging, Detection, And Ranging (LIDAR) technology including [249]–[252] that deliver centimeter-level mapping accuracy to cost-effective smartphone-based solutions that achieve meter-level accuracy.”