ALGD-ORB: An improved image feature extraction algorithm with adaptive threshold and local gray difference

Chu, Guoming; Peng, Yan; Luo, Xuhong

doi:10.1371/journal.pone.0293111

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…However, due to its computational intensity, the Speeded-Up Robust Feature (SURF) has been proposed and applied [ 20 ]. After that, ORB (Oriented FAST and Rotated BRIEF) was proposed [ 21 ], which is more computationally efficient than its predecessor.…”

Section: Related Workmentioning

confidence: 99%

ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry

Huang,

Chen,

Zhang

et al. 2024

Sensors

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Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.

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Section: Related Workmentioning

confidence: 99%

ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry

Huang,

Chen,

Zhang

et al. 2024

Sensors

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PE-SLAM: A Modified Simultaneous Localization and Mapping System Based on Particle Swarm Optimization and Epipolar Constraints

Li,

Shang,

Wang

et al. 2024

Applied Sciences

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Due to various typical unstructured factors in the environment of photovoltaic power stations, such as high feature similarity, weak textures, and simple structures, the motion model of the ORB-SLAM2 algorithm performs poorly, leading to a decline in tracking accuracy. To address this issue, we propose PE-SLAM, which improves the ORB-SLAM2 algorithm’s motion model by incorporating the particle swarm optimization algorithm combined with epipolar constraint to eliminate mismatches. First, a new mutation strategy is proposed to introduce perturbations to the pbest (personal best value) during the late convergence stage of the PSO algorithm, thereby preventing the PSO algorithm from falling into local optima. Then, the improved PSO algorithm is used to solve the fundamental matrix between two images based on the feature matching relationships obtained from the motion model. Finally, the epipolar constraint is applied using the computed fundamental matrix to eliminate incorrect matches produced by the motion model, thereby enhancing the tracking accuracy and robustness of the ORB-SLAM2 algorithm in unstructured photovoltaic power station scenarios. In feature matching experiments, compared to the ORB algorithm and the ORB+HAMMING algorithm, the ORB+PE-match algorithm achieved an average accuracy improvement of 19.5%, 14.0%, and 6.0% in unstructured environments, respectively, with better recall rates. In the trajectory experiments of the TUM dataset, PE-SLAM reduced the average absolute trajectory error compared to ORB-SLAM2 by 29.1% and the average relative pose error by 27.0%. In the photovoltaic power station scene mapping experiment, the dense point cloud map constructed has less overlap and is complete, reflecting that PE-SLAM has basically overcome the unstructured factors of the photovoltaic power station scene and is suitable for applications in this scene.

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ALGD-ORB: An improved image feature extraction algorithm with adaptive threshold and local gray difference

Cited by 2 publications

References 44 publications

ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry

ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry

PE-SLAM: A Modified Simultaneous Localization and Mapping System Based on Particle Swarm Optimization and Epipolar Constraints

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