YOLO-SLAM: A semantic SLAM system towards dynamic environment with geometric constraint

Wenxin, Wu; Guo, Lan-Ping; Gao, Hongli; You, Zhichao; Liu, Yuekai; Chen, Zhiqiang

doi:10.1007/s00521-021-06764-3

Cited by 108 publications

(58 citation statements)

References 38 publications

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“…ORB-SLAM2 4 DS-SLAM 11 Sun et al 30 Blitz-SLAM 21 Crowd-SLAM 27 YOLO-SLAM 24 Ours Fig. 8 The visual comparison of ATE between our method and ORB-SLAM2 on the Fr3_w_xyz: (a) our method and (b) ORB-SLAM2.…”

Section: Accuracy Comparison With Other Systemsmentioning

confidence: 96%

“…Table 6 shows RMSE comparisons of ATE of our approach with ORB-SLAM2 4 and other five VSLAM frameworks applying semantic segmentation or object detection networks: DS-SLAM, 11 Sun et al, 30 Blitz-SLAM, 21 Crowd-SLAM, 27 and YOLO-SLAM. 24 We also analyzed the improvement of our system compared with ORB-SLAM2. It can be seen from the results in Table 6 that our method has achieved the best results in three of the four sequences, and the overall performance is better than other dynamic VSLAM systems.…”

Section: Accuracy Comparison With Other Systemsmentioning

confidence: 99%

“…It can be seen from the figures that our method is evidently superior to ORB-SLAM2 in system accuracy. The reason for this situation is that the presence of short-term dynamic elements seriously affects the Table 7 Comparison of our method with ORB-SLAM2, 4 DS-SLAM, 11 Sun et al, 30 Blitz-SLAM, 21 Crowd-SLAM, 27 and YOLO-SLAM 24 on RMSE of ATE, and the improvements of our method relative to ORB-SLAM2 on the TUM RGB-D datasets. The best results for each sequence are marked in bold.…”

Section: Accuracy Comparison With Other Systemsmentioning

confidence: 99%

“…ORB-SLAM2 4 DS-SLAM 11 Sun et al 30 Blitz-SLAM 21 Crowd-SLAM 27 YOLO-SLAM 24 Ours Table 8 Comparison of our method with ORB-SLAM2, 4 DS-SLAM, 11 Sun et al, 30 Blitz-SLAM, 21 Crowd-SLAM, 27 and YOLO-SLAM 24 on RMSE of translational drift (RPE), and the improvements of our method relative to ORB-SLAM2 on the TUM RGB-D datasets. The best results for each sequence are marked in bold.…”

Section: Accuracy Comparison With Other Systemsmentioning

confidence: 99%

“…ORB-SLAM2 4 DS-SLAM 11 Sun et al 30 Blitz-SLAM 21 Crowd-SLAM 27 YOLO-SLAM 24 Ours reliability of feature points and causes large deviations in the subsequent pose estimation, so a reasonable and effective dynamic points detection and rejection strategy are necessary. ORB-SLAM2 does not have a corresponding elimination strategy, and our system solves this problem, thus greatly improving the system accuracy.…”

Section: Accuracy Comparison With Other Systemsmentioning

confidence: 99%

See 4 more Smart Citations

Eliminating short-term dynamic elements for robust visual simultaneous localization and mapping using a coarse-to-fine strategy

Yang

Zhang

et al. 2022

J. Electron. Imag.

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. Visual simultaneous localization and mapping (VSLAM) is one of the foremost principal technologies for intelligent robots to implement environment perception. Many research works have focused on proposing comprehensive and integrated systems based on the static environment assumption. However, the elements whose motion status changes frequently, namely short-term dynamic elements, can significantly affect the system performance. Therefore, it is extremely momentous to cope with short-term dynamic elements to make the VSLAM system more adaptable to dynamic scenes. This paper proposes a coarse-to-fine elimination strategy for short-term dynamic elements based on motion status check (MSC) and feature points update (FPU). First, an object detection module is designed to obtain semantic information and screen out the potential short-term dynamic elements. And then an MSC module is proposed to judge the true status of these elements and thus ultimately determine whether to eliminate them. In addition, an FPU module is introduced to update the extracted feature points according to calculating the dynamic region factor to improve the robustness of VSLAM system. Quantitative and qualitative experiments on two challenging public datasets are performed. The results demonstrate that our method effectively eliminates the influence of short-term dynamic elements and outperforms other state-of-the-art methods.

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Section: Accuracy Comparison With Other Systemsmentioning

confidence: 96%

Section: Accuracy Comparison With Other Systemsmentioning

confidence: 99%

Section: Accuracy Comparison With Other Systemsmentioning

confidence: 99%

Section: Accuracy Comparison With Other Systemsmentioning

confidence: 99%

Section: Accuracy Comparison With Other Systemsmentioning

confidence: 99%

See 3 more Smart Citations

Eliminating short-term dynamic elements for robust visual simultaneous localization and mapping using a coarse-to-fine strategy

Yang

Zhang

et al. 2022

J. Electron. Imag.

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MVS‐SLAM: Enhanced multiview geometry for improved semantic RGBD SLAM in dynamic environment

Islam,

Ibrahim,

Chin

et al. 2023

Journal of Field Robotics

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Simultaneous Localization and Mapping (SLAM) is a crucial technology for intelligent mobile robots to operate successfully in unknown environments. While many excellent SLAM systems have been developed in recent years, most assume that the environment is static, resulting in poor performance in dynamic environments. To address this limitation, we propose multiview stereo (MVS)‐SLAM (MVS‐SLAM), a real‐time semantic RGBD SLAM system with improved‐multiview geometry, built on the RGBD mode of ORB‐SLAM3. MVS‐SLAM tightly integrates semantic and geometric information to tackle the challenges posed by dynamic scenes. To meet the real‐time requirements, the semantic module leverages the latest and fastest object detection network, YOLOv7, to provide semantic prior knowledge for the geometric module. We introduce a novel geometric constraint method that capitalizes on depth images and semantic information to recover three‐dimensional (3D) feature points and initial camera pose. We use a 3D coordinate error threshold to identify dynamic points and remove them using the K‐means clustering algorithm. This approach effectively reduces the impact of dynamic points. We validate MVS‐SLAM using challenging dynamic sequences from the TUM data set, demonstrating that it significantly improves localization accuracy and system robustness in all types of dynamic environments.

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UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

Wang,

Kooistra,

Pan

et al. 2024

Journal of Field Robotics

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This study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)‐based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV‐based SLAM. A scoping review was carried out to identify the key concepts and applications, and discover research gaps in the use of algorithm‐oriented and task‐oriented, open‐source studies. A total of 97 studies met the criteria after conducting a two‐step screening by a systematic method followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. Among eligible studies, 97 were classified into two main categories: algorithm‐oriented studies and task‐oriented studies. The analysis of the literature revealed that the majority of the studies were focused on the development and implementation of new algorithms and algorithms. This review highlights the significance and diversity of sensors utilized in UAVs in different tasks and applications scenarios that employ different types of sensors. The evaluation method is able to show the real results and performance of the new algorithms in the target scenarios compared with the evaluation method by the public data set and simulation platform.

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YOLO-SLAM: A semantic SLAM system towards dynamic environment with geometric constraint

Cited by 108 publications

References 38 publications

Eliminating short-term dynamic elements for robust visual simultaneous localization and mapping using a coarse-to-fine strategy

Eliminating short-term dynamic elements for robust visual simultaneous localization and mapping using a coarse-to-fine strategy

MVS‐SLAM: Enhanced multiview geometry for improved semantic RGBD SLAM in dynamic environment

UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

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