Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images-Reference-Cited by-同舟云学术

Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images

Published:2022-07 Issue:3 Volume:7 Page:7597-7604
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Guadagnino Tiziano¹^ORCID,Chen Xieyuanli²^ORCID,Sodano Matteo²^ORCID,Behley Jens²^ORCID,Grisetti Giorgio¹^ORCID,Stachniss Cyrill²^ORCID

Affiliation:

1. La Sapienza University of Rome, Rome, Italy

2. University of Bonn, Bonn, Germany

Funder

Deutsche Forschungsgemeinschaft

Germany’s Excellence Strategy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9750005/09801638.pdf?arnumber=9801638

Reference35 articles.

1. Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment

2. Collar Line Segments for fast odometry estimation from Velodyne point clouds

3. Least-squares estimation of transformation parameters between two point patterns

4. 3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs

5. LOAM: Lidar Odometry and Mapping in Real-time

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