# UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry

arXiv: https://arxiv.org/abs/2205.08150 · HTML: https://mlsanity.com/paper/2205.08150/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [FlowNet3D: Learning Scene Flow in 3D Point Clouds](https://mlsanity.com/paper/1806.01411/) (2018)
- [PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency](https://mlsanity.com/paper/2103.05465/) (2021)
- [LO-Net: Deep Real-time Lidar Odometry](https://mlsanity.com/paper/1904.08242/) (2019)
- [PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization](https://mlsanity.com/paper/2012.00972/) (2020)
- [DMLO: Deep Matching LiDAR Odometry](https://mlsanity.com/paper/2004.03796/) (2020)
- [LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation](https://mlsanity.com/paper/2009.00164/) (2020)
- [Self-supervised Learning of LiDAR Odometry for Robotic Applications](https://mlsanity.com/paper/2011.05418/) (2020)
- [DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network](https://mlsanity.com/paper/1910.11088/) (2019)
- [CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR](https://mlsanity.com/paper/1712.06352/) (2017)
- [SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks](https://mlsanity.com/paper/2010.09343/) (2020)
- [DeepLO: Geometry-Aware Deep LiDAR Odometry](https://mlsanity.com/paper/1902.10562/) (2019)
- [UnDeepLIO: Unsupervised Deep Lidar-Inertial Odometry](https://mlsanity.com/paper/2109.01533/) (2021)

## Built on by (most cited first, 1 of 1)

- [Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps](https://mlsanity.com/paper/2301.08957/) (2023)

## Abstract (arXiv)

High-precision lidar odomety is an essential part of autonomous driving. In recent years, deep learning methods have been widely used in lidar odomety tasks, but most of the current methods only extract the global features of the point clouds. It is impossible to obtain more detailed point-level features in this way. In addition, only the fully connected layer is used to estimate the pose. The fully connected layer has achieved obvious results in the classification task, but the changes in pose are a continuous rather than discrete process, high-precision pose estimation can not be obtained only by using the fully connected layer. Our method avoids problems mentioned above. We use PointPWC as our backbone network. PointPWC is originally used for scene flow estimation. The scene flow estimation task has a strong correlation with lidar odomety. Traget point clouds can be obtained by adding the scene flow and source point clouds. We can achieve the pose directly through ICP algorithm solved by SVD, and the fully connected layer is no longer used. PointPWC extracts point-level features from point clouds with different sampling levels, which solves the problem of too rough feature extraction. We conduct experiments on KITTI, Ford Campus Vision and Lidar DataSe and Apollo-SouthBay Dataset. Our result is comparable with the state-of-the-art unsupervised deep learing method SelfVoxeLO.