DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
| Autor: | Bescos, Berta, Fácil, José M., Civera, Javier, Neira, José |
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| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | IEEE Robotics and Automation Letters ( Volume: 3, Issue: 4, Oct. 2018 ) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/LRA.2018.2860039 |
| Popis: | The assumption of scene rigidity is typical in SLAM algorithms. Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments, which are the target of several relevant applications like service robotics or autonomous vehicles. In this paper we present DynaSLAM, a visual SLAM system that, building over ORB-SLAM2 [1], adds the capabilities of dynamic object detection and background inpainting. DynaSLAM is robust in dynamic scenarios for monocular, stereo and RGB-D configurations. We are capable of detecting the moving objects either by multi-view geometry, deep learning or both. Having a static map of the scene allows inpainting the frame background that has been occluded by such dynamic objects. We evaluate our system in public monocular, stereo and RGB-D datasets. We study the impact of several accuracy/speed trade-offs to assess the limits of the proposed methodology. DynaSLAM outperforms the accuracy of standard visual SLAM baselines in highly dynamic scenarios. And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments. Comment: This work has been accepted at IEEE Robotics and Automation Letters, and will be presented at the IEEE Conference on Intelligent Robots and Systems 2018 |
| Databáze: | arXiv |
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