Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes

Zhang, Xinguang; Zhang, Ruidong; Wang, Xiankun

doi:10.3390/app122211548

Cited by 22 publications

(21 citation statements)

References 36 publications

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“…This does not guarantee the accuracy of its own motion estimation, the dynamic feature point determination accuracy is unstable, and it does not solve the problem of large positioning errors when dynamic objects occupy the main body. While Zhang et al [25] also combined YOLOv5 with an ORB-SLAM2 front-end, the difference is that they only rejected dynamic feature points for potential dynamic points within the dynamic box based on the optical flow method's relative transformation and polar line constraints. Although these two methods can effectively remove the interference of dynamic objects to some extent, they both do not make effective use of the results of target detection and perform redundant calculations on some feature points, which reduces the SLAM system's operational efficiency.…”

Section: Object-detection-based Methodsmentioning

confidence: 99%

“…Because the algorithm used in this paper is based on ORB-SLAM3, the performance of this paper is first compared to the original ORB-SLAM3 algorithm. Then, it is compared to the current high-performing semantic SLAM algorithms DS-SLAM and DynaSLAM for dynamic scenes, as well as two recent object-detection-based SLAM algorithms, DO-SLAM and [25]. The comparison results between the algorithms in this paper and ORB-SLAM3 are presented first.…”

Section: Dynamic Feature Point Eliminationmentioning

confidence: 99%

“…This is because these two methods perform more complex processing when estimating their own motion. For example, [25] first used the pyramid optical flow method to perform optical flow calculations for different pyramid layers based on the characteristics of the adjacent frames: constant luminance, temporal continuity, and spatial consistency. The feature points in the image are then tracked using the pyramidal optical flow to determine their correspondence.…”

Section: Dynamic Feature Point Eliminationmentioning

confidence: 99%

“…As shown in Figure 5, the sparse point cloud map of ORB-SLAM3 itself only s the location information of feature points in the scene, which only possesses simple metric information with a poor readability, making it difficult for the robot to reus sparse point cloud map to accomplish more advanced tasks, such as obstacle avoid Table 6 compares the APE results of this paper's method to DS-SLAM, DynaSLAM, DO-SLAM and [25]. The accuracy performance of this paper's algorithm in the fr3_walking_xyz data sequence is superior to DS-SLAM and DynaSLAM, while in the fr3_walking_halfsphere data sequence, it is slightly inferior to those two algorithms.…”

Section: Static Dense Map Constructionmentioning

confidence: 99%

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A Dynamic Scene Vision SLAM Method Incorporating Object Detection and Object Characterization

Guan

Qian

et al. 2023

Sustainability

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Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in real-world dynamic scenarios. To improve the robustness and performance of SLAM systems in dynamic environments, this paper proposes a new RGB-D SLAM method for indoor dynamic scenes based on object detection. The method presented in this paper improves on the ORB-SLAM3 framework. First, we designed an object detection module based on YOLO v5 and relied on it to improve the tracking module of ORB-SLAM3 and the localization accuracy of ORB-SLAM3 in dynamic environments. The dense point cloud map building module was also included, which excludes dynamic objects from the environment map to create a static environment point cloud map with high readability and reusability. Full comparison experiments with the original ORB-SLAM3 and two representative semantic SLAM methods on the TUM RGB-D dataset show that: the method in this paper can run at 30+fps, the localization accuracy improved to varying degrees compared to ORB-SLAM3 in all four image sequences, and the absolute trajectory accuracy can be improved by up to 91.10%. The localization accuracy of the method in this paper is comparable to that of DS-SLAM, DynaSLAM and the two recent target detection-based SLAM algorithms, but it runs faster. The RGB-D SLAM method proposed in this paper, which combines the most advanced object detection method and visual SLAM framework, outperforms other methods in terms of localization accuracy and map construction in a dynamic indoor environment and has a certain reference value for navigation, localization, and 3D reconstruction.

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Section: Object-detection-based Methodsmentioning

confidence: 99%

Section: Dynamic Feature Point Eliminationmentioning

confidence: 99%

Section: Dynamic Feature Point Eliminationmentioning

confidence: 99%

Section: Static Dense Map Constructionmentioning

confidence: 99%

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A Dynamic Scene Vision SLAM Method Incorporating Object Detection and Object Characterization

Guan

Qian

et al. 2023

Sustainability

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show abstract

“…However, the accuracy of dynamic region detection can be improved. Zhang et al [ 17 ] employed the YOLOv5 to detect dynamic information, and integrated the optical flow technique for a second detection pass to enhance detection precision. They subsequently constructed a global map by utilizing keyframes and removing highly dynamic objects.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments

Zang

Zhang

2023

Sensors

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Recent developments in robotics have heightened the need for visual SLAM. Dynamic objects are a major problem in visual SLAM which reduces the accuracy of localization due to the wrong epipolar geometry. This study set out to find a new method to address the low accuracy of visual SLAM in outdoor dynamic environments. We propose an adaptive feature point selection system for outdoor dynamic environments. Initially, we utilize YOLOv5s with the attention mechanism to obtain a priori dynamic objects in the scene. Then, feature points are selected using an adaptive feature point selector based on the number of a priori dynamic objects and the percentage of a priori dynamic objects occupied in the frame. Finally, dynamic regions are determined using a geometric method based on Lucas-Kanade optical flow and the RANSAC algorithm. We evaluate the accuracy of our system using the KITTI dataset, comparing it to various dynamic feature point selection strategies and DynaSLAM. Experiments show that our proposed system demonstrates a reduction in both absolute trajectory error and relative trajectory error, with a maximum reduction of 39% and 30%, respectively, compared to other systems.

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