GMSK-SLAM: a new RGB-D SLAM method with dynamic areas detection towards dynamic environments

Wei, Hongyu; Zhang, Tao; Zhang, Liang

doi:10.1007/s11042-021-11168-5

Cited by 16 publications

(15 citation statements)

References 36 publications

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“…In [12], the epipolar geometric constraint is employed for the initial identification of static map points, and the conditional random field method is subsequently applied to further refine the recognition results. Meanwhile, GMSK-SLAM [33] introduces a grid-based feature point matching approach and a dynamic area detection method that combines K-means clustering. This combination enhances both accuracy and robustness, particularly in dynamic environments.…”

Section: Moving Consistency Methodsmentioning

confidence: 99%

Robust RGB: D-SLAM in highly dynamic environments based on probability observations and clustering optimization

Liu,

Tian,

et al. 2023

Meas. Sci. Technol.

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Visual simultaneous localization and mapping (SLAM) is the underlying support of unmanned systems. Currently, most visual SLAM methods are based on the static environment assumption so that dynamic objects in the camera’s field of view will seriously disrupt its working performance. In view of this, an RGB-D SLAM approach based on probability observations and clustering optimization for highly dynamic environments is proposed, which can effectively eliminate the influence of dynamic objects and accurately estimate the ego-motion of an RGB-D camera. The method contains a dual static map point detection strategy, which is carried out simultaneously in the current and previous frames. First, to enhance tracking robustness in highly dynamic environments, the probabilities of map points being static, calculated by both reprojection deviation and intensity deviation, are used to weight the cost function for pose estimation. Meanwhile, by taking the previous frames as a reference, a static velocity probability based on sparse scene flow is acquired to preliminarily recognize static map points and further improve the tracking accuracy. Then, an improved map point optimization strategy based on K-means clustering is designed, which effectively takes advantage of the clustering algorithm to refine the static map point labels while alleviating its stubborn problem. Finally, the experimental results on the TUM dataset and real scenes compared with the state-of-the-art visual SLAM methods illustrate that the proposed method achieves an extremely robust and accurate performance for estimating camera pose in highly dynamic environments.

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Section: Moving Consistency Methodsmentioning

confidence: 99%

Robust RGB: D-SLAM in highly dynamic environments based on probability observations and clustering optimization

Liu,

Tian,

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…This method does not rely on a priori knowledge and does not require an initialization process, which can remove moving objects to a certain extent. Wei et al [15] proposed an RGB-D SLAM method for dynamic region detection, which focuses more on the dynamic region than on the dynamic object, combining a grid-based motion statistics method with the Kmeans [16] clustering algorithm to distinguish the dynamic region from the image to preserving the static information in the dynamic environment effectively increases the number of reliable feature points.…”

Section: B Geometry-based Methodsmentioning

confidence: 99%

A Dense Visual SLAM Method in Dynamic Scenes

Liu,

Zhang,

Wang

et al. 2023

IEEE Access

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The visual SLAM algorithm has been developed to be more mature. It can achieve good performance in most static scenes, but when it is applied to dynamic scenes, it will be interfered with by a large number of dynamic points in the scene, which affects the system accuracy and even leads to tracking failures, and the sparse maps built by traditional SLAM algorithms can not satisfy advanced needs such as navigation. Aiming at the above problems, this paper proposes a dense visual SLAM method in dynamic scenes. In this paper, based on the ORB-SLAM2 algorithm, we use the improved balanced quadtree method to obtain uniformly distributed feature points, use the motion grid statistics method to improve the accuracy of feature matching between neighboring frames, add a new target detection network to obtain the a priori information of the dynamic objects, and then construct a dense map containing only static information in the dense map building thread. Finally, to verify the effectiveness of the algorithms in this paper, the TUM dataset is used to compare with the algorithms of ORB-SLAM2, ORB-SLAM3, DS-SLAM, and RDS-SLAM. The results show that this paper's SLAM algorithm improves 97.54%, 97.35%, 28.74%, and 17.37% on the root mean square error of the absolute trajectory error compared to the four algorithms in the measured highdynamic sequences, respectively.

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“…Researchers have worked to improve the robustness of visual SLAM systems in dynamic environments, for which various innovative algorithms have been proposed. Among them, Wei H et al [8] proposed GMSK-SLAM, which focuses on RGB-D dynamic region detection in dynamic environments and greatly improves the localization accuracy in dynamic environments. However, this algorithm has obvious problems in the accuracy of feature-based SLAM subsequent localization and map building.…”

Section: Related Workmentioning

confidence: 99%

Highly Dynamic Visual SLAM Dense Map Construction Based on Indoor Environments

Mei,

Zuo,

Song

2024

IEEE Access

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In indoor highly dynamic scenes or scenes with missing a priori dynamic information, most current schemes cannot effectively avoid the impact of moving objects on the performance of SLAM (Simultaneous Localization and Mapping) systems. In order to solve the problem of accurate localization and map building for mobile robots in indoor highly dynamic environments, we propose a dynamic RGB-D visual SLAM dense map building method based on pyramidal L-K (Lucas-Kanade) optical flow with multi-view geometric constraints. Our proposed method consists of three stages: dynamic object culling, camera position estimation, and dense map construction based on the TSDF (truncated signed distance function) model. First, dynamic elements in the scene are detected and culled by combining pyramidal L-K optical flow with a multiview geometric constraint method. Then, the estimation of the camera pose is achieved by minimizing the SDF error function. Finally, the estimated camera poses and static depth images are used to construct TSDF dense maps and indexed using dynamic voxel assignment and spatial hashing techniques. We evaluated our method on dynamic sequences of the Bonn dataset and the TUM dataset, and proved the effectiveness of the algorithm on a real scene dataset of our own making. The experimental results show that our method can effectively improve the camera position estimation accuracy and realize the construction of dense maps after processing dynamic objects, which improves the robustness of the system as well as the accuracy of environment reconstruction.

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GMSK-SLAM: a new RGB-D SLAM method with dynamic areas detection towards dynamic environments

Cited by 16 publications

References 36 publications

Robust RGB: D-SLAM in highly dynamic environments based on probability observations and clustering optimization

Robust RGB: D-SLAM in highly dynamic environments based on probability observations and clustering optimization

A Dense Visual SLAM Method in Dynamic Scenes

Highly Dynamic Visual SLAM Dense Map Construction Based on Indoor Environments

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