Corpus ID: 235727638

F-LOAM: Fast LiDAR Odometry And Mapping

@article{Wang2021FLOAMFL,
  title={F-LOAM: Fast LiDAR Odometry And Mapping},
  author={Han Wang and Chen Wang and Chunlin Chen and Lihua Xie},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.00822}
}
  • Han Wang, Chen Wang, +1 author Lihua Xie
  • Published 2021
  • Computer Science
  • ArXiv
Simultaneous Localization and Mapping (SLAM) has wide robotic applications such as autonomous driving and unmanned aerial vehicles. Both computational efficiency and localization accuracy are of great importance towards a good SLAM system. Existing works on LiDAR based SLAM often formulate the problem as two modules: scan-to-scan match and scan-to-map refinement. Both modules are solved by iterative calculation which are computationally expensive. In this paper, we propose a general solution… Expand

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References

SHOWING 1-10 OF 30 REFERENCES
A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping
TLDR
A complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities is given. Expand
LOL: Lidar-only Odometry and Localization in 3D point cloud maps*
TLDR
This paper integrates a state-of-the-art Lidaronly odometry algorithm with a recently proposed 3D point segment matching method by complementing their advantages and demonstrates the utility of the proposed LOL system on several Kitti datasets of different lengths and environments. Expand
IMLS-SLAM: Scan-to-Model Matching Based on 3D Data
  • Jean-Emmanuel Deschaud
  • Computer Science, Engineering
  • 2018 IEEE International Conference on Robotics and Automation (ICRA)
  • 2018
TLDR
This work presents a new low-drift SLAM algorithm based only on 3D LiDAR data that relies on a scan-to-model matching framework and uses the Implicit Moving Least Squares (IMLS) surface representation. Expand
LOAM: Lidar Odometry and Mapping in Real-time
TLDR
The method achieves both low-drift and low-computational complexity without the need for high accuracy ranging or inertial measurements and can achieve accuracy at the level of state of the art offline batch methods. Expand
Low-drift and real-time lidar odometry and mapping
TLDR
The results indicate that the proposed method for low-drift odometry and mapping using range measurements from a 3D laser scanner moving in 6-DOF can achieve accuracy comparable to the state of the art offline, batch methods. Expand
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
  • Tixiao Shan, Brendan Englot
  • Computer Science
  • 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2018
TLDR
A lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles and integrated into a SLAM framework to eliminate the pose estimation error caused by drift is integrated. Expand
LIMO: Lidar-Monocular Visual Odometry
TLDR
A depth extraction algorithm from LIDAR measurements for camera feature tracks and estimating motion by robustified keyframe based Bundle Adjustment is proposed, and semantic labeling is used for outlier rejection and weighting of vegetation landmarks. Expand
Real-time loop closure in 2D LIDAR SLAM
TLDR
This work presents the approach used in the backpack mapping platform which achieves real-time mapping and loop closure at a 5 cm resolution and provides experimental results and comparisons to other well known approaches which show that, in terms of quality, this approach is competitive with established techniques. Expand
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
TLDR
A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. Expand
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building.Expand
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