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Related Publications

Incorporating Learnt Local and Global Embeddings into Monocular Visual SLAM

Huaiyang Huang, Haoyang Ye, Yuxiang Sun, Lujia Wang, Ming Liu

Autonomous Robots (AURO), Sprinter, 2021

website code paper bib

@article{huang2021incorporating, title={Incorporating learnt local and global embeddings into monocular visual SLAM}, author={Huang, Huaiyang and Ye, Haoyang and Sun, Yuxiang and Wang, Lujia and Liu, Ming}, journal={Autonomous Robots}, volume={45}, number={6}, pages={789--803}, year={2021}, publisher={Springer} }

Monocular Visual Odometry using Learned Repeatability and Description

Huaiyang Huang, Haoyang Ye, Yuxiang Sun, Ming Liu

IEEE International Conference on Robotics and Automation (ICRA), 2020

website code paper bib

@inproceedings{huang2020monocular, title={Monocular visual odometry using learned repeatability and description}, author={Huang, Huaiyang and Ye, Haoyang and Sun, Yuxiang and Liu, Ming}, booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)}, pages={8913--8919}, year={2020}, organization={IEEE} }

Abstract

Traditional approaches for Visual Simultaneous Localization and Mapping (VSLAM) rely on low-level vision information for state estimation, such as handcrafted local features or the image gradient. While significant progress has been made through this track, under more challenging configuration for monocular VSLAM, e.g., varying illumination, the performance of state-of-the-art systems generally degrades. As a consequence, robustness and accuracy for monocular VSLAM are still widely concerned. This paper presents a monocular VSLAM system that fully exploits learnt features for better state estimation. The proposed system leverages both learnt local features and global embeddings at different modules of the system: direct camera pose estimation, inter-frame feature association, and loop closure detection. With a probabilistic explanation of keypoint prediction, we formulate the camera pose tracking in a direct manner and parameterize local features with uncertainty taken into account. To alleviate the quantization effect, we adapt the mapping module to generate 3D landmarks better to guarantee the system's robustness. Detecting temporal loop closure via deep global embeddings further improves the robustness and accuracy of the proposed system. The proposed system is extensively evaluated on public datasets (Tsukuba, EuRoC, and KITTI), and compared against the state-of-the-art methods. The competitive performance of camera pose estimation confirms the effectiveness of our method.

Method

Proposed frame tracking scheme.

Demo Videos

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Experiments on Tsukuba Lamps and EuRoC V101.