Publication detail
CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
VEĽAS, M. ŠPANĚL, M. HRADIŠ, M. HEROUT, A.
Original Title
CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
English Title
CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
Type
conference paper
Language
en
Original Abstract
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.
English abstract
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.
Keywords
ground segmentation, LiDAR, Velodyne, convolutional neural network
Released
27.04.2018
Publisher
Institute of Electrical and Electronics Engineers
Location
Torres Vedras
ISBN
978-1-5386-5221-3
Book
IEEE International Conference on Autonomous Robot Systems and Competitions
Edition
NEUVEDEN
Edition number
NEUVEDEN
Pages from
71
Pages to
77
Pages count
7
URL
Documents
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",
annote="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.",
address="Institute of Electrical and Electronics Engineers",
booktitle="IEEE International Conference on Autonomous Robot Systems and Competitions",
chapter="157179",
doi="10.1109/ICARSC.2018.8374163",
edition="NEUVEDEN",
howpublished="online",
institution="Institute of Electrical and Electronics Engineers",
number="4",
year="2018",
month="april",
pages="71--77",
publisher="Institute of Electrical and Electronics Engineers",
type="conference paper"
}