Title
Lo-net: Deep real-time lidar odometry
Document Type
Conference Proceeding
Publication Date
6-1-2019
Abstract
We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Unlike 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 benchmark datasets demonstrate that LO-Net outperforms existing learning based approaches and has similar accuracy with the state-of-the-art geometry-based approach, LOAM.
Publication Source (Journal or Book title)
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
First Page
8465
Last Page
8474
Recommended Citation
Li, Q., Chen, S., Wang, C., Li, X., Wen, C., Cheng, M., & Li, J. (2019). Lo-net: Deep real-time lidar odometry. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 8465-8474. https://doi.org/10.1109/CVPR.2019.00867