Robust and Fast Collaborative Augmented Reality Framework Based on Monocular SLAM

Kam, Jaewon; Kim, Hansol; Lee, Sang-Jun; Hwang, Sung Soo

doi:10.5573/ieiespc.2020.9.4.325

Cited by 2 publications

(1 citation statement)

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“…However, the monocular SLAM can obtain only image information without scale information, so it is more dependent on the quality of the image. erefore, how to improve the robustness of monocular SLAM under di erent disturbances is a very challenging and important task in this eld [11,12]. ere are three main implementation schemes in visual SLAM, namely feature-based method, direct method, and semidirect method.…”

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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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

View full text Add to dashboard Cite

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An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments

Wang

et al. 2023

Applied Sciences

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The visual simultaneous localization and mapping (SLAM) method under dynamic environments is a hot and challenging issue in the robotic field. The oriented FAST and Rotated BRIEF (ORB) SLAM algorithm is one of the most effective methods. However, the traditional ORB-SLAM algorithm cannot perform well in dynamic environments due to the feature points of dynamic map points at different timestamps being incorrectly matched. To deal with this problem, an improved visual SLAM method built on ORB-SLAM3 is proposed in this paper. In the proposed method, an improved new map points screening strategy and the repeated exiting map points elimination strategy are presented and combined to identify obvious dynamic map points. Then, a concept of map point reliability is introduced in the ORB-SLAM3 framework. Based on the proposed reliability calculation of the map points, a multi-period check strategy is used to identify the unobvious dynamic map points, which can further deal with the dynamic problem in visual SLAM, for those unobvious dynamic objects. Finally, various experiments are conducted on the challenging dynamic sequences of the TUM RGB-D dataset to evaluate the performance of our visual SLAM method. The experimental results demonstrate that our SLAM method can run at an average time of 17.51 ms per frame. Compared with ORB-SLAM3, the average RMSE of the absolute trajectory error (ATE) of the proposed method in nine dynamic sequences of the TUM RGB-D dataset can be reduced by 63.31%. Compared with the real-time dynamic SLAM methods, the proposed method can obtain state-of-the-art performance. The results prove that the proposed method is a real-time visual SLAM, which is effective in dynamic environments.

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Robust and Fast Collaborative Augmented Reality Framework Based on Monocular SLAM

Cited by 2 publications

References 0 publications

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

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

An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments

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