Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation
arXiv:1902.09103
Abstract
Accurate relative pose is one of the key components in visual odometry (VO) and simultaneous localization and mapping (SLAM). Recently, the self-supervised learning framework that jointly optimizes the relative pose and target image depth has attracted the attention of the community. Previous works rely on the photometric error generated from depths and poses between adjacent frames, which contains large systematic error under realistic scenes due to reflective surfaces and occlusions. In this paper, we bridge the gap between geometric loss and photometric loss by introducing the matching loss constrained by epipolar geometry in a self-supervised framework. Evaluated on the KITTI dataset, our method outperforms the state-of-the-art unsupervised ego-motion estimation methods by a large margin. The code and data are available at https://github.com/hlzz/DeepMatchVO.
Accepted by ICRA 2019
References in corpus (9)
- Adam: A Method for Stochastic Optimization
- Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
- Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
- DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
- DeMoN: Depth and Motion Network for Learning Monocular Stereo
- SfM-Net: Learning of Structure and Motion from Video
- Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
- GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
- BA-Net: Dense Bundle Adjustment Network