Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICARSC.2018.8374163 | 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) |
Keywords | DocType | Volume |
velodyne LiDAR,convolutional neural networks,3D LiDAR scans,original sparse data,translational motion parameters,real-time performance,IMU support,high quality odometry estimation,LiDAR data registration,rotational motion parameters,LOAM | Conference | abs/1712.06352 |
ISSN | ISBN | Citations |
2573-9360 | 978-1-5386-5222-0 | 6 |
PageRank | References | Authors |
0.51 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Velas | 1 | 7 | 1.20 |
| Michal Spanel | 2 | 41 | 7.85 |
| Michal Hradis | 3 | 132 | 14.19 |
| Adam Herout | 4 | 248 | 35.39 |