An efficient edge-feature constraint visual SLAM

Liu, Wenjun; Mo, Yaokai; Jiao, Jichao

doi:10.1145/3371425.3371455

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Simultaneous Localization and Mapping (SLAM) means that the subject equipped with sensors builds an environmental map and estimates its own motion through anaylyzing the subject's motion in an unknown environment [1] . In recent years, scholars have focused their research on visual SLAM [2][3] .…”

Section: Introductionmentioning

confidence: 99%

RGB-D visual SLAM for point association local edge features

Hongtu,

Fang,

Yunjiang

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Aiming at the difficulty of point feature matching in 3D reconstruction to meet the tracking requirements of weakly textured scenes, this paper proposes a visual SLAM algorithm based on grid method combining points with edge features. In the tracking thread, a method based on grid method is proposed to evaluate the feature quality of points. The textures of external environment are judged according to ORB feature description, and the information of Canny edge features of weakly textured mesh is added to improve the positioning accuracy. In the local mapping thread, the joint feature points pose and map points are iteratively optimized to improve the convergence rate of the algorithm. The simulation results show that the proposed algorithm has a good location and tracking effects in the weak texture scene.

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Section: Introductionmentioning

confidence: 99%

RGB-D visual SLAM for point association local edge features

Hongtu,

Fang,

Yunjiang

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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“…In order to enhance robot's autonomy and robustness, facilitate more complex tasks, move from path-planning to task-planning, and enable advanced human-robot interaction, the importance of works of semantics of environments have been recognized. Based on the work [26], we continue to introduces semantics to solve the aforementioned problem of features located at the edges of objects and propose an effective edge-feature processing method for this. Inspired by Occam's Razor [27], we call our method Edge-Feature Razor, or EF-Razor for short.…”

Section: Introductionmentioning

confidence: 99%

EF-Razor: An Effective Edge-Feature Processing Method in Visual SLAM

Liu

Jiao

et al. 2020

IEEE Access

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Feature-based visual simultaneous localization and mapping (SLAM) is an effective localization approach for robots in unknown environments. Classic handcrafted features perform well in 2D image matching tasks. However, in the tracking task of SLAM, the region at the edge of the object in the image is often unstable because of the lack of spatial information. In this paper, we refer to the features at the edge of the object as edge-features and propose an effective method to process the edge-features in SLAM named Edge-Feature Razor (EF-Razor) for the above problems. EF-Razor first uses the semantics provided by the object detection YOLOv3 to distinguish edge-features. Through additional constraints on edgefeatures matching in the tracking process, EF-Razor can effectively reduce the impact of unstable features on the SLAM system. Then, EF-Razor adjusts the information matrix to increase the system's trust in the filtered features. This will make the calculation result of the bundle adjustment more stable. In order to evaluate the proposed method, we integrate EF-Razor to ORB-SLAM2 and perform experiments. The comparison results based on public datasets show the proposed method could effectively reduce the absolute trajectory error by 7%. INDEX TERMS edge, features, object detection, simultaneous localization and mapping.

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An experimental evaluation of Siamese Neural Networks for robot localization using omnidirectional imaging in indoor environments

Cabrera,

Román,

Gil

et al. 2024

Artif Intell Rev

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The objective of this paper is to address the localization problem using omnidirectional images captured by a catadioptric vision system mounted on the robot. For this purpose, we explore the potential of Siamese Neural Networks for modeling indoor environments using panoramic images as the unique source of information. Siamese Neural Networks are characterized by their ability to generate a similarity function between two input data, in this case, between two panoramic images. In this study, Siamese Neural Networks composed of two Convolutional Neural Networks (CNNs) are used. The output of each CNN is a descriptor which is used to characterize each image. The dissimilarity of the images is computed by measuring the distance between these descriptors. This fact makes Siamese Neural Networks particularly suitable to perform image retrieval tasks. First, we evaluate an initial task strongly related to localization that consists in detecting whether two images have been captured in the same or in different rooms. Next, we assess Siamese Neural Networks in the context of a global localization problem. The results outperform previous techniques for solving the localization task using the COLD-Freiburg dataset, in a variety of lighting conditions, specially when using images captured in cloudy and night conditions.

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An efficient edge-feature constraint visual SLAM

Cited by 4 publications

References 25 publications

RGB-D visual SLAM for point association local edge features

RGB-D visual SLAM for point association local edge features

EF-Razor: An Effective Edge-Feature Processing Method in Visual SLAM

An experimental evaluation of Siamese Neural Networks for robot localization using omnidirectional imaging in indoor environments

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