A Real-Time Dynamic Object Segmentation Framework for SLAM System in Dynamic Scenes

Chang, Jianfang; Dong, Na; Li, Donghui

doi:10.1109/tim.2021.3109718

Cited by 33 publications

(15 citation statements)

References 40 publications

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“…However, in reality, sensor measurement errors, system modeling errors, complex environmental dynamics and unrealistic constraints (conditions) affect the accuracy and reliability of manually designed systems [31]. Although modern vision SLAM systems are quite mature and have satisfactory performance [32], the aforementioned classical SLAM systems are with the assumption that the objects for SLAM are static, and the detection and processing of dynamic objects are very limited.…”

Section: A Motivationmentioning

confidence: 99%

A Robust Visual SLAM Method for Additive Manufacturing of Vehicular Parts Under Dynamic Scenes

Wen-bo

Fan

et al. 2023

IEEE Access

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Section: A Motivationmentioning

confidence: 99%

A Robust Visual SLAM Method for Additive Manufacturing of Vehicular Parts Under Dynamic Scenes

Wen-bo

Fan

et al. 2023

IEEE Access

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“…Rosinol [26] et al introduced 3D dynamic scene graphs (DSG) to recognize semantic information in dynamic environments, implementing an automated method for constructing DSG from visual inertial data via Kimera. Chang [27] et al introduced the YOLACT network for instance segmentation of dynamic objects, used multi-view geometry to reject dynamic feature points outside the mask, and used adaptive thresholding optical flow method to further reject dynamic feature points. Li [28] et al propose a visual SLAM system based on sparse features, which is based on the results of semantic segmentation and geometric constraints combined with Bayesian probability estimation to track dynamic feature points.…”

Section: Relateo Workmentioning

confidence: 99%

Dynamic Semantics SLAM Based on Improved Mask R-CNN

2022

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Simulation localization and mapping(SLAM) is a popular research problem in the field of driverless cars, but there are still some difficult problems to solve. The conventional SLAM algorithm does not take into account the dynamic objects in the environment, resulting in problems such as low bit pose estimation. In this paper, we provide a deep learning-based SLAM scheme. In order to solve the problem of inaccurate feature point extraction in a dynamic environment, this paper adopts image pyramid to distribute feature points uniformly and extracts feature points by using the adaptive thresholding method. To address the problem of incomplete dynamic object mask segmentation, an improved Mask R-CNN network is proposed to improve the integrity of the mask edges by adding an edge detection end to the Mask R-CNN network. To address the problem of incomplete dynamic feature point rejection, this paper uses a motion consistency detection algorithm to detect dynamic feature points, and uses the remaining static features for bit pose estimation. The experimental results on the TUM dataset show that the absolute trajectory error of the SLAM algorithm in this paper is reduced by 93.2% on average compared to the ORB-SLAM2 algorithm. Compared with the Dyna-SLAM algorithm, the absolute trajectory error of the algorithm in this paper is reduced by 36.6% on average.

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“…Simultaneous Localization and Mapping (SLAM) plays essential roles in robotic and other related elds [1][2][3]. In the robotic eld, SLAM systems are used to solve the problem of robots about where they are.…”

Section: Introductionmentioning

confidence: 99%

A Variable Radius Side Window Direct SLAM Method Based on Semantic Information

Chen

Mutabazi

et al. 2022

Computational Intelligence and Neuroscience

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Simultaneous Localization and Mapping (SLAM) is a challenging and key issue in the mobile robotic fields. In terms of the visual SLAM problem, the direct methods are more suitable for more expansive scenes with many repetitive features or less texture in contrast with the feature-based methods. However, the robustness of the direct methods is weaker than that of the feature-based methods. To deal with this problem, an improved direct sparse odometry with loop closure (LDSO) is proposed, where the performance of the SLAM system under the influence of different imaging disturbances of the camera is focused on. In the proposed method, a method based on the side window strategy is proposed for preprocessing the input images with a multilayer stacked pixel blender. Then, a variable radius side window strategy based on semantic information is proposed to reduce the weight of selected points on semistatic objects, which can reduce the computation and improve the accuracy of the SLAM system based on the direct method. Various experiments are conducted on the KITTI dataset and TUM RGB-D dataset to test the performance of the proposed method under different camera imaging disturbances. The quantitative and qualitative evaluations show that the proposed method has better robustness than the state-of-the-art direct methods in the literature. Finally, a real-world experiment is conducted, and the results prove the effectiveness of the proposed method.

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A Real-Time Dynamic Object Segmentation Framework for SLAM System in Dynamic Scenes

Cited by 33 publications

References 40 publications

A Robust Visual SLAM Method for Additive Manufacturing of Vehicular Parts Under Dynamic Scenes

A Robust Visual SLAM Method for Additive Manufacturing of Vehicular Parts Under Dynamic Scenes

Dynamic Semantics SLAM Based on Improved Mask R-CNN

A Variable Radius Side Window Direct SLAM Method Based on Semantic Information

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