Detail publikace

CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR

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"
}

Dokumenty

cnn-odometry-estimation-print.pdf