Sparse-Dense Motion Modelling and Tracking for Manipulation Without Prior Object Models

Rauch, Christian; Long, Ran; Ivan, Vladimir; Vijayakumar, Sethu

doi:10.1109/lra.2022.3200177

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Co-Fusion (CF) [4] can further reconstruct the regions of outliers and track them independently. Similarly, MultiMo-tionFusion [15] can reconstruct dynamic objects online once they are detected and can re-detect them after they reappear in the camera view with the object models. Multimotion visual odometry (MVO) proposes an online multimotion pipeline to simultaneously estimate the segmentation of multiple dynamic objects and estimate their rigid motions independently.…”

Section: A Visual Dynamic Slammentioning

confidence: 99%

RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

Long¹,

Rauch²,

Ivan³

et al. 2023

Preprint

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This work presents a novel RGB-D-inertial dynamic SLAM method that can enable accurate localisation when the majority of the camera view is occluded by multiple dynamic objects over a long period of time. Most dynamic SLAM approaches either remove dynamic objects as outliers when they account for a minor proportion of the visual input, or detect dynamic objects using semantic segmentation before camera tracking. Therefore, dynamic objects that cause large occlusions are difficult to detect without prior information. The remaining visual information from the static background is also not enough to support localisation when large occlusion lasts for a long period. To overcome these problems, our framework presents a robust visual-inertial bundle adjustment that simultaneously tracks camera, estimates cluster-wise dense segmentation of dynamic objects and maintains a static sparse map by combining dense and sparse features. The experiment results demonstrate that our method achieves promising localisation and object segmentation performance compared to other state-of-the-art methods in the scenario of long-term large occlusion.

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Section: A Visual Dynamic Slammentioning

confidence: 99%

RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

Long¹,

Rauch²,

Ivan³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Visual SLAM Integration With Semantic Segmentation and Deep Learning: A Review

Pu,

Luo,

Wang

et al. 2023

IEEE Sensors J.

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

Long

Rauch

Zhang

et al. 2022

IEEE Robot. Autom. Lett.

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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.

show abstract

Sparse-Dense Motion Modelling and Tracking for Manipulation Without Prior Object Models

Cited by 3 publications

References 18 publications

RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

Visual SLAM Integration With Semantic Segmentation and Deep Learning: A Review

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

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