RGB-D SLAM in Dynamic Environments Using Static Point Weighting

Li, Shile; Lee, Dongheui

doi:10.1109/lra.2017.2724759

Cited by 227 publications

(105 citation statements)

References 26 publications

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“…Motion Segmentation DSLAM [14] Motion Removal DVO-SLAM [16] DynaSLAM (N+G) (RGB-D) SLAM [12] w RGB-D ORB-SLAM2 is initialized and starts the tracking from the very first frame, and hence dynamic objects can introduce errors. ORB-SLAM delays the initialization until there is parallax and consensus using the staticity assumption.…”

Section: Sequence Depth Edgementioning

confidence: 99%

DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes

Bescos

Fácil

Civera

et al. 2018

IEEE Robot. Autom. Lett.

849

569

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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 on 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. Dy-naSLAM 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.

show abstract

Section: Sequence Depth Edgementioning

confidence: 99%

DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes

Bescos

Fácil

Civera

et al. 2018

IEEE Robot. Autom. Lett.

849

569

View full text Add to dashboard Cite

show abstract

“…Furthermore, different failure cases are thoroughly demonstrated, throwing some light into possible future work. For future work, it will be promising to improve our current method to be more robust to dynamic environments such as using weighted edge points [47]. Moreover, implementing our current method on a GPU will be more efficient [41] and it will also be interesting to integrate our method for a robot autonomous navigation system [48].…”

Section: Discussionmentioning

confidence: 99%

Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

Meng

Tung

Little

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Camera relocalization plays a vital role in many robotics and computer vision applications, such as self-driving cars and virtual reality. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these point features are only sampled randomly in images, without considering geometric information such as lines, leading to large errors with the existence of poorly textured areas or in motion blur. Line segments are more robust in these environments. In this work, we propose to jointly exploit points and lines within the framework of uncertainty driven regression forests. The proposed approach is thoroughly evaluated on three publicly available datasets against several strong state-of-the-art baselines in terms of several different error metrics. Experimental results prove the efficacy of our method, showing superior or on-par state-ofthe-art performance.

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“…Then a region growing procedure is performed to identify dynamic regions by adapting these seeds. Li et al [22] proposed a RGB-D SLAM method based on depth edge for dynamic environments in 2017. Only depth edge points are used to estimate the camera motion.…”

Section: Related Workmentioning

confidence: 99%

Dynamic-DSO: Direct Sparse Odometry Using Objects Semantic Information for Dynamic Environments

et al. 2020

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Traditional Simultaneous Localization and Mapping (SLAM) (with loop closure detection), or Visual Odometry (VO) (without loop closure detection), are based on the static environment assumption. When working in dynamic environments, they perform poorly whether using direct methods or indirect methods (feature points methods). In this paper, Dynamic-DSO which is a semantic monocular direct visual odometry based on DSO (Direct Sparse Odometry) is proposed. The proposed system is completely implemented with the direct method, which is different from the most current dynamic systems combining the indirect method with deep learning. Firstly, convolutional neural networks (CNNs) are applied to the original RGB image to generate the pixel-wise semantic information of dynamic objects. Then, based on the semantic information of the dynamic objects, dynamic candidate points are filtered out in keyframes candidate points extraction; only static candidate points are reserved in the tracking and optimization module, to achieve accurate camera pose estimation in dynamic environments. The photometric error calculated by the projection points in dynamic region of subsequent frames are removed from the whole photometric error in pyramid motion tracking model. Finally, the sliding window optimization which neglects the photometric error calculated in the dynamic region of each keyframe is applied to obtain the precise camera pose. Experiments on the public TUM dynamic dataset and the modified Euroc dataset show that the positioning accuracy and robustness of the proposed Dynamic-DSO is significantly higher than the state-of-the-art direct method in dynamic environments, and the semi-dense cloud map constructed by Dynamic-DSO is clearer and more detailed.

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RGB-D SLAM in Dynamic Environments Using Static Point Weighting

Cited by 227 publications

References 26 publications

DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes

DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes

Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

Dynamic-DSO: Direct Sparse Odometry Using Objects Semantic Information for Dynamic Environments

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