Joint Self-Supervised Monocular Depth Estimation and SLAM | |
Xing, Xiaoxia1,2![]() ![]() ![]() ![]() ![]() | |
2022 | |
会议名称 | International Conference on Pattern Recognition (ICPR) |
会议日期 | Aug. 21-25, 2022 |
会议地点 | Montréal Québec, Canada |
摘要 | Classical monocular Simultaneous Localization and Mapping (SLAM) and convolutional neural networks (CNNs) based monocular depth estimation represent two different methods towards reconstructing the 3D geometry of the scene. In this paper, we leverage SLAM and depth estimation for their respective advantages to further improve the performance of both tasks. For SLAM, running pseudo RGBD-SLAM with CNN predicted depths improves the accuracy of visual odometry and mapping compared with the monocular SLAM baseline. For depth estimation, we use 3D scene structures from geometric SLAM to refine the pre-trained monocular depth estimation network to update the model which did not reach the optimum due to the photometric inconsistency. Moreover, the proposed method adds an optional Sparse Auxiliary Network into the original depth estimation network, from which the sparse depth features are dynamically combined with RGB features for predicting depth map. Experimental results on KITTI and TUM RGB-D datasets show that our method achieves state-of-the-art performances on both depth predictions and pose estimations. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48802 |
专题 | 综合信息系统研究中心_视知觉融合及其应用 |
通讯作者 | Cai, Yinghao |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.University of Chinese Academy of Sciences, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Xing, Xiaoxia,Cai, Yinghao,Lu, Tao,et al. Joint Self-Supervised Monocular Depth Estimation and SLAM[C],2022. |
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