UFSM_VO: Stereo Odometry Based on Uniformly Feature Selection and Strictly Correspondence Matching
文献类型：会议论文
| 作者 | Liangliang Pan; Jun Cheng; Qieshi Zhang |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 英文摘要 | Robust visual feature plays a critical role in improving camera localization performance. However, it will cost much computation time for feature extracting and matching, such as SIFT or SURF. In this paper, we present a novel visual odometry (VO) algorithm based on stereo image sequences by performing uniformly feature selection and strict correspondence matching. Firstly, the stable and uniform feature selection is performed by setting adaptive feature thresholds and selecting limited number of features in each local region. Secondly, the precise correspondence matching is achieved by double verification based on the motion model. Finally, the translation vector and rotation matrix of camera are computed, the five-point method is combined with RANSAC-based outlier rejection scheme for initial rotation estimation. And then all inliers are used for minimizing reprojection error to get final camera pose. The experimental results show that the proposed method can achieve the average translational error lower than 1.16% with 12Hz on the public KITTI dataset [1]. |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13781]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Liangliang Pan,Jun Cheng,Qieshi Zhang. UFSM_VO: Stereo Odometry Based on Uniformly Feature Selection and Strictly Correspondence Matching[C]. 见:. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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