YPD-SLAM: A Real-Time VSLAM System for Handling Dynamic Indoor Environments

Wang, Yi; Bu, Haoyu; Zhang, Xiaolong; Jia, Chen

doi:10.3390/s22218561

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…To improve the real-time performance of dynamic VSLAM, some researchers have used lightweight object detection algorithms to recognize dynamic objects. Wang [23], Cheng [24], and Zhao et al [25] combined a short-time object detection algorithm with epipolar geometry to judge dynamic feature points in the environment by comparing the distance between the feature points and polar lines in the detection box. However, the epipolar geometry has a limited ability to identify small displacement objects.…”

Section: Methods Based On Deep Learning and Geometric Information Fusionmentioning

confidence: 99%

YDD-SLAM: Indoor Dynamic Visual SLAM Fusing YOLOv5 with Depth Information

Cong,

Liu,

et al. 2023

Sensors

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Simultaneous location and mapping (SLAM) technology is key in robot autonomous navigation. Most visual SLAM (VSLAM) algorithms for dynamic environments cannot achieve sufficient positioning accuracy and real-time performance simultaneously. When the dynamic object proportion is too high, the VSLAM algorithm will collapse. To solve the above problems, this paper proposes an indoor dynamic VSLAM algorithm called YDD-SLAM based on ORB-SLAM3, which introduces the YOLOv5 object detection algorithm and integrates deep information. Firstly, the objects detected by YOLOv5 are divided into eight subcategories according to their motion characteristics and depth values. Secondly, the depth ranges of the dynamic object and potentially dynamic object in the moving state in the scene are calculated. Simultaneously, the depth value of the feature point in the detection box is compared with that of the feature point in the detection box to determine whether the point is a dynamic feature point; if it is, the dynamic feature point is eliminated. Further, multiple feature point optimization strategies were developed for VSLAM in dynamic environments. A public data set and an actual dynamic scenario were used for testing. The accuracy of the proposed algorithm was significantly improved compared to that of ORB-SLAM3. This work provides a theoretical foundation for the practical application of a dynamic VSLAM algorithm.

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Section: Methods Based On Deep Learning and Geometric Information Fusionmentioning

confidence: 99%

YDD-SLAM: Indoor Dynamic Visual SLAM Fusing YOLOv5 with Depth Information

Cong,

Liu,

et al. 2023

Sensors

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“…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. Song et al [ 22 ] introduced YF-SLAM, which utilizes the lightweight target detection network YOLO-FastestV2 to provide semantic information in dynamic environments for ORB-SLAM2.…”

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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“…Figure 3 displays the network structure of the Yolo-Fastest V2 model, whose report studies are still few [26][27][28][29]. Instead of using the original backbone network, the model adopts the ShuffleNet V2 [30] network for the backbone feature extraction, reducing the memory access cost.…”

Section: Yolo-series Neural Network Trainingmentioning

confidence: 99%

Embedded Yolo-Fastest V2-Based 3D Reconstruction and Size Prediction of Grain Silo-Bag

Guo,

Mao,

Dai

et al. 2023

Remote Sensing

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Contactless and non-destructive measuring tools can facilitate the moisture monitoring of bagged or bulk grain during transportation and storage. However, accurate target recognition and size prediction always impede the effectiveness of contactless monitoring in actual use. This paper developed a novel 3D reconstruction method upon multi-angle point clouds using a binocular depth camera and a proper Yolo-based neural model to resolve the problem. With this method, this paper developed an embedded and low-cost monitoring system for the in-warehouse grain bags, which predicted targets’ 3D size and boosted contactless grain moisture measuring. Identifying and extracting the object of interest from the complex background was challenging in size prediction of the grain silo-bag on a conveyor. This study first evaluated a series of Yolo-based neural network models and explored the most appropriate neural network structure for accurately extracting the grain bag. In point-cloud processing, this study constructed a rotation matrix to fuse multi-angle point clouds to generate a complete one. This study deployed all the above methods on a Raspberry Pi-embedded board to perform the grain bag’s 3D reconstruction and size prediction. For experimental validation, this study built the 3D reconstruction platform and tested grain bags’ reconstruction performance. First, this study determined the appropriate positions (−60°, 0°, 60°) with the least positions and high reconstruction quality. Then, this study validated the efficacy of the embedded system by evaluating its speed and accuracy and comparing it to the original Torch model. Results demonstrated that the NCNN-accelerated model significantly enhanced the average processing speed, nearly 30 times faster than the Torch model. The proposed system predicted the objects’ length, width, and height, achieving accuracies of 97.76%, 97.02%, and 96.81%, respectively. The maximum residual value was less than 9 mm. And all the root mean square errors were less than 7 mm. In the future, the system will mount three depth cameras for achieving real-time size prediction and introduce a contactless measuring tool to finalize grain moisture detection.

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YPD-SLAM: A Real-Time VSLAM System for Handling Dynamic Indoor Environments

Cited by 7 publications

References 37 publications

YDD-SLAM: Indoor Dynamic Visual SLAM Fusing YOLOv5 with Depth Information

YDD-SLAM: Indoor Dynamic Visual SLAM Fusing YOLOv5 with Depth Information

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

Embedded Yolo-Fastest V2-Based 3D Reconstruction and Size Prediction of Grain Silo-Bag

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