Sitpose: A Siamese Convolutional Transformer for Relative Camera Pose Estimation

摘要

Relative Camera Pose Estimation (RCPE) aims to calculate the translation and rotation between two frames with overlapped regions, which is crucial to computer vision and robotics. This paper presents a novel siamese convolutional transformer model, SiTPose, to regress relative camera pose directly. SiTPose is distinguished in three aspects: (1) With a cross-attention feature extractor and a compact transformer encoder, extreme rotation errors (> 150°) are significantly reduced: from 9.7‰ with the state-of-the-art 8-Points to 1‱ on the 7Scenes dataset. (2) SiTPose is also robust to narrow-baseline cases (slight rotation angle and large translation between neighboring frames), while existing RCPE methods mainly focus on wide-baseline cases. (3) SiTPose can be flexibly extended to geometry-based vSLAM (namely SiTSLAM) in a multi-threaded way to prevent tracking lost and scale ambiguity problems. Results on multiple datasets show that SiT-SLAM yields a marked improvement in robustness and localization accuracy in complex scenarios, e.g., RMSE error is reduced from 26.36m with the classic ORBSLAM3 method to 6.94m on the KITTI-09.