XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization for XR Applications
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913675742281728 |
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| author | Zhai, Shangjin Wang, Nan Wang, Xiaomeng Chen, Danpeng Xie, Weijian Bao, Hujun Zhang, Guofeng |
| author_facet | Zhai, Shangjin Wang, Nan Wang, Xiaomeng Chen, Danpeng Xie, Weijian Bao, Hujun Zhang, Guofeng |
| contents | This paper presents a novel approach to Visual Inertial Odometry (VIO), focusing on the initialization and feature matching modules. Existing methods for initialization often suffer from either poor stability in visual Structure from Motion (SfM) or fragility in solving a huge number of parameters simultaneously. To address these challenges, we propose a new pipeline for visual inertial initialization that robustly handles various complex scenarios. By tightly coupling gyroscope measurements, we enhance the robustness and accuracy of visual SfM. Our method demonstrates stable performance even with only four image frames, yielding competitive results. In terms of feature matching, we introduce a hybrid method that combines optical flow and descriptor-based matching. By leveraging the robustness of continuous optical flow tracking and the accuracy of descriptor matching, our approach achieves efficient, accurate, and robust tracking results. Through evaluation on multiple benchmarks, our method demonstrates state-of-the-art performance in terms of accuracy and success rate. Additionally, a video demonstration on mobile devices showcases the practical applicability of our approach in the field of Augmented Reality/Virtual Reality (AR/VR). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization for XR Applications Zhai, Shangjin Wang, Nan Wang, Xiaomeng Chen, Danpeng Xie, Weijian Bao, Hujun Zhang, Guofeng Computer Vision and Pattern Recognition This paper presents a novel approach to Visual Inertial Odometry (VIO), focusing on the initialization and feature matching modules. Existing methods for initialization often suffer from either poor stability in visual Structure from Motion (SfM) or fragility in solving a huge number of parameters simultaneously. To address these challenges, we propose a new pipeline for visual inertial initialization that robustly handles various complex scenarios. By tightly coupling gyroscope measurements, we enhance the robustness and accuracy of visual SfM. Our method demonstrates stable performance even with only four image frames, yielding competitive results. In terms of feature matching, we introduce a hybrid method that combines optical flow and descriptor-based matching. By leveraging the robustness of continuous optical flow tracking and the accuracy of descriptor matching, our approach achieves efficient, accurate, and robust tracking results. Through evaluation on multiple benchmarks, our method demonstrates state-of-the-art performance in terms of accuracy and success rate. Additionally, a video demonstration on mobile devices showcases the practical applicability of our approach in the field of Augmented Reality/Virtual Reality (AR/VR). |
| title | XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization for XR Applications |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.01297 |