Prof. Ing. Adam Herout, Ph.D.

VEĽAS Martin, ŠPANĚL Michal, HRADIŠ Michal and HEROUT Adam. CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR. In: Proceedings of IEEE International Conference on Autonomous Robot Systems and Competitions. Torres Vedras: Institute of Electrical and Electronics Engineers, 2018, pp. 71-77. ISBN 978-1-5386-5221-3. Available from: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8374163&isnumber=8374143
Publication language:english
Original title:CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
Title (cs):Konvoluční sítě pro odhad odometrie v datech LiDARu Velodyne za pomoci IMU senzoru
Pages:71-77
Proceedings:Proceedings of IEEE International Conference on Autonomous Robot Systems and Competitions
Conference:IEEE International Conference on Autonomous Robot Systems and Competitions
Place:Torres Vedras, PT
Year:2018
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8374163&isnumber=8374143
ISBN:978-1-5386-5221-3
DOI:10.1109/ICARSC.2018.8374163
Publisher:Institute of Electrical and Electronics Engineers
Keywords
ground segmentation, LiDAR, Velodyne, convolutional neural network
Annotation
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.
BibTeX:
@INPROCEEDINGS{
   author = {Martin Ve{\'{l}}as and Michal {\v{S}}pan{\v{e}}l
	and Michal Hradi{\v{s}} and Adam Herout},
   title = {CNN for IMU Assisted Odometry Estimation using
	Velodyne LiDAR},
   pages = {71--77},
   booktitle = {Proceedings of IEEE International Conference on Autonomous
	Robot Systems and Competitions},
   year = 2018,
   location = {Torres Vedras, PT},
   publisher = {Institute of Electrical and Electronics Engineers},
   ISBN = {978-1-5386-5221-3},
   doi = {10.1109/ICARSC.2018.8374163},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=11527}
}

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