4.6 Article

RGB-D SLAM in Indoor Planar Environments With Multiple Large Dynamic Objects

期刊

IEEE ROBOTICS AND AUTOMATION LETTERS
卷 7, 期 3, 页码 8209-8216

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3186091

关键词

SLAM; visual tracking; sensor fusion

类别

资金

  1. European Union [101017008]
  2. Alan Turing Institute

向作者/读者索取更多资源

This work presents a novel dense RGB-D SLAM approach for dynamic planar environments that enables simultaneous multi-object tracking, camera localisation, and background reconstruction. The approach utilizes camera motion prior to perform dense SLAM even when the camera view is largely occluded by multiple dynamic objects.
This work presents a novel dense RGB-D SLAM approach for dynamic planar environments that enables simultaneous multi-object tracking, camera localisation and background reconstruction. Previous dynamic SLAM methods either rely on semantic segmentation to directly detect dynamic objects; or assume that dynamic objects occupy a smaller proportion of the camera view than the static background and can, therefore, be removed as outliers. With the aid of camera motion prior, our approach enables dense SLAM when the camera view is largely occluded by multiple dynamic objects. The dynamic planar objects are separated by their different rigid motions and tracked independently. The remaining dynamic non-planar areas are removed as outliers and not mapped into the background. The evaluation demonstrates that our approach outperforms the state-of-the-art methods in terms of localisation, mapping, dynamic segmentation and object tracking. We also demonstrate its robustness to large drift in the camera motion prior.

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