Name
Abstract | ||
|---|---|---|
We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Un-like most existing lidar odometry (LO) estimations that go through individually designed feature selection, feature matching, and pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a new mask-weighted geometric constraint loss, LO-Net can effectively learn feature representation for LO estimation, and can implicitly exploit the sequential dependencies and dynamics in the data. We also design a scan-to-map module, which uses the geometric and semantic information learned in LO-Net, to improve the estimation accuracy. Experiments on bench-mark datasets demonstrate that LO-Net outperforms existing learning based approaches and has similar accuracy with the state-of-the-art geometry-based approach, LOAM. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.00867 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
DocType | Volume | ISSN |
Journal | abs/1904.08242 | 1063-6919 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qing Li | 1 | 0 | 0.68 |
| Shaoyang Chen | 2 | 0 | 0.34 |
| Cheng Wang | 3 | 218 | 32.63 |
| Xin Li | 4 | 258 | 19.84 |
| Chenglu Wen | 5 | 4 | 1.74 |
| Ming Cheng | 6 | 54 | 13.93 |
| Jonathan Li | 7 | 798 | 119.18 |