Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints

作者：You-Yi Jau, Rui Zhu, Hao Su, Manmohan Chandraker · 年份：2020 · DOI：10.1109/iros45743.2020.9341229 · 被引用次数：1 · 研究领域：Robotics and Sensor-Based Localization、Advanced Vision and Imaging、Image and Object Detection Techniques

Estimating relative camera poses from consecutive frames is a fundamental problem in visual odometry (VO) and simultaneous localization and mapping (SLAM), where classic methods consisting of hand-crafted features and sampling-based outlier rejection have been a dominant choice for over a decade. Although multiple works propose to replace these modules with learning-based counterparts, most have not yet been as accurate, robust and generalizable as conventional methods. In this paper, we design an end-to-end trainable framework consisting of learnable modules for detection, feature extraction, matching and outlier rejection, while directly optimizing for the geometric pose objective. We show both quantitatively and qualitatively that pose estimation performance may be achieved on par with the classic pipeline. Moreover, we are able to show by end-to-end training, the key components of the pipeline could be significantly improved, which leads to better generalizability to unseen datasets compared to existing learning-based methods.