Publication Details
Convolutional Neural Networks for the Odometry Estimation
Španěl Michal, Ing., Ph.D. (DCGM)
Hradiš Michal, Ing., Ph.D. (DCGM)
Herout Adam, prof. Ing., Ph.D. (DCGM)
Odometry, Velodyne, LiDAR, CNN, KITTI
This article presents a novel method for odometry estimation from 3D data of Velodyne LiDAR scanner using convolutional neural networks. For training and forward evaluation of the proposed networks, the original data is encoded into 2D matrices. In experiments with the KITTI dataset, our networks show significantly higher accuracy in estimation of the translational motion parameters compared to the state of the art LOAM method. In addition, they achieve higher speed and real-time performance. Using data provided by the IMU sensor, it is possible to estimate odometry and align the point cloud with a high precision. The proposed method can replace the odometry estimation from the wheel encoders or supplement the missing GPS data when the GNSS signal is not available (for example, during the interior mapping). In addition, we propose alternate CNNs for the estimation of the rotational motion that achieve results comparable to the state of the art. Our solution delivers real-time performance and accuracy to provide online preview of the mapping and to verify the completeness of the map during the mission.
@article{BUT162266,
author="Martin {Veľas} and Michal {Španěl} and Michal {Hradiš} and Adam {Herout}",
title="Convolutional Neural Networks for the Odometry Estimation",
journal="Journal of Intelligent and Robotics Systems",
year="2019",
volume="2019",
number="93",
pages="1--22",
issn="0921-0296",
url="https://www.fit.vut.cz/research/publication/11763/"
}