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VEĽAS, M. ŠPANĚL, M. HRADIŠ, M. HEROUT, A.
Originální název
CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
We introduce a novel method for odometry estimation using convolutional neural networks from 3D LiDAR scans. The original sparse data are encoded into 2D matrices for the training of proposed networks and for the prediction. Our networks show significantly better precision in the estimation of translational motion parameters comparing with state of the art method LOAM, while achieving real-time performance. Together with IMU support, high quality odometry estimation and LiDAR data registration is realized. Moreover, we propose alternative CNNs trained for the prediction of rotational motion parameters while achieving results also comparable with state of the art. The proposed method can replace wheel encoders in odometry estimation or supplement missing GPS data, when the GNSS signal absents (e.g. during the indoor mapping). Our solution brings real-time performance and precision which are useful to provide online preview of the mapping results and verification of the map completeness in real time.
Klíčová slova
ground segmentation, LiDAR, Velodyne, convolutional neural network
Autoři
VEĽAS, M.; ŠPANĚL, M.; HRADIŠ, M.; HEROUT, A.
Vydáno
27. 4. 2018
Nakladatel
Institute of Electrical and Electronics Engineers
Místo
Torres Vedras
ISBN
978-1-5386-5221-3
Kniha
IEEE International Conference on Autonomous Robot Systems and Competitions
ISSN
2573-9387
Ročník
2018
Číslo
4
Strany od
71
Strany do
77
Strany počet
7
URL
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8374163&isnumber=8374143
BibTex
@inproceedings{BUT157179, author="Martin {Veľas} and Michal {Španěl} and Michal {Hradiš} and Adam {Herout}", title="CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR", booktitle="IEEE International Conference on Autonomous Robot Systems and Competitions", year="2018", volume="2018", number="4", pages="71--77", publisher="Institute of Electrical and Electronics Engineers", address="Torres Vedras", doi="10.1109/ICARSC.2018.8374163", isbn="978-1-5386-5221-3", issn="2573-9387", url="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8374163&isnumber=8374143" }