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

Guan, Hao; Qian, Chengyuan; Wu, Tingsong; Hu, Xiaoming; Duan, Fuzhou; Ye, Xinyi

doi:10.3390/su15043048

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…Zhang et al [ 19 ] augmented the ORB-SLAM2 system with a YOLOv5-based object detection and recognition module, achieving real-time and rapid detection of dynamic features. Guan et al [ 20 ] incorporated a YOLOv5 target detection module into the tracking module of ORB-SLAM3 and generated static environment point cloud maps using RGB-D cameras. Wang et al [ 21 ] proposed YPD-SLAM, a system based on Yolo-FastestV2 target detection and CAPE plane extraction, capable of running on the CPU while maintaining relatively high detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots

Xie,

Qin,

Zhang

et al. 2024

Sensors

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Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network, GY, based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and the SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While mAP@0.5 increased by 0.514% to 95.364, the model simultaneously reduced the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system localization accuracy and robustness. It shows a remarkable 92.59% improvement in RMSE for Absolute Trajectory Error (ATE), along with a 93.11% improvement in RMSE for the translational drift of Relative Pose Error (RPE) and a 92.89% improvement in RMSE for the rotational drift of RPE. Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts.

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Section: Related Workmentioning

confidence: 99%

GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots

Xie,

Qin,

Zhang

et al. 2024

Sensors

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“…Guan H. et al [19] use ORB-SLAM3 [20] as the backbone of a dynamic SLAM method. They use YOLOv5 [21] to detect objects and remove image features which are on the dynamic object areas.…”

Section: B Dynamic Visual Slam Without Dynamic Object Motionmentioning

confidence: 99%

“…Equation ( 21) requires Jacobian matrix for the LM nonlinear optimization. Projection error   ,  defined by (19) has unknown parameters of dynamic map point, dynamic motion transformation, and keyframe pose. In OMBA step, the keyframe pose is set as constant parameters, but in FBA step, it is reset as unknown parameters.…”

Section: Local Dynamic Bamentioning

confidence: 99%

TDO-SLAM: Traffic Sign and Dynamic Object Based Visual SLAM

Park,

Lee

2024

IEEE Access

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This paper introduces a real-time visual SLAM system, TDO-SLAM, using only a stereo vision camera. TDO-SLAM works not only in static but also in dynamic road environment by incorporating the object motion and the planar property of standing traffic signs. Traditional visual SLAM systems assume that the road environment is static. However, a variety of dynamic objects exist in the real-world urban environment. Thus, the traditional SLAM systems are subject to fail due to the various motion of the dynamic objects. To solve this inherent problem in the dynamic environment, TDO-SLAM detects, tracks, and manages the global object identification of dynamic objects and standing traffic signs through a novel Object-Level-Tracking method. We improve the accuracy of camera pose estimation through several steps of bundle adjustments, including the residual terms for the planar constraint of traffic signs and the dynamic object motion. Experimental results show that pose estimation accuracy is improved in complex environment with several dynamic objects and traffic signs. Performance of TDO-SLAM is analyzed and compared with ORB-SLAM2, ORB-SLAM3, and DynaSLAM using three benchmark datasets, KITTI Odometry dataset, KITTI Raw dataset, and Complex Urban dataset.

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“…It is the most representative method in feature point SLAM. In recent years, based on ORB-SLAM, a lot of studies have emerged around dynamic scenes [12][13][14]. Among them, their aim is mainly to eliminate dynamic feature points by constructing relevant methods with semantic constraints through deep learning.…”

Section: Introductionmentioning

confidence: 99%

AHY-SLAM: Toward Faster and More Accurate Visual SLAM in Dynamic Scenes Using Homogenized Feature Extraction and Object Detection Method

Gong

et al. 2023

Sensors

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At present, SLAM is widely used in all kinds of dynamic scenes. It is difficult to distinguish dynamic targets in scenes using traditional visual SLAM. In the matching process, dynamic points are incorrectly added to the pose calculation with the camera, resulting in low precision and poor robustness in the pose estimation. This paper proposes a new dynamic scene visual SLAM algorithm based on adaptive threshold homogenized feature extraction and YOLOv5 object detection, named AHY-SLAM. This new method adds three new modules based on ORB-SLAM2: a keyframe selection module, a threshold calculation module, and an object detection module. The optical flow method is used to screen keyframes for each frame input in AHY-SLAM. An adaptive threshold is used to extract feature points for keyframes, and dynamic points are eliminated with YOLOv5. Compared with ORB-SLAM2, AHY-SLAM has significantly improved pose estimation accuracy over multiple dynamic scene sequences in the TUM open dataset, and the absolute pose estimation accuracy can be increased by up to 97%. Compared with other dynamic scene SLAM algorithms, the speed of AHY-SLAM is also significantly improved under a guarantee of acceptable accuracy.

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

Cited by 11 publications

References 32 publications

GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots

GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots

TDO-SLAM: Traffic Sign and Dynamic Object Based Visual SLAM

AHY-SLAM: Toward Faster and More Accurate Visual SLAM in Dynamic Scenes Using Homogenized Feature Extraction and Object Detection Method

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