MATCHING FILTER-BASED VSLAM OPTIMIZATION IN INDOOR ENVIRONMENTS

An important factor that reduced the accuracy of motion trajectories in existing VSLAM (Visual Simultaneous Localization and Mapping) systems is the poor estimation of the position pose of the vision odometer. The existing methods generate many incorrect matches during the feature matching process,...

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Bibliographic Details
Published in:International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLVIII-1/W2-2023; pp. 679 - 685
Main Authors: Wei, S., Wang, S.
Format: Journal Article Conference Proceeding
Language:English
Published: Gottingen Copernicus GmbH 01.01.2023
Copernicus Publications
Subjects:
Accuracy
Algorithms
Cameras
Cloud computing
Consistency
Estimation
Geometric transformation
Indoor environments
Matching
Navigation
Random sampling
Simultaneous localization and mapping
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ISSN:2194-9034, 1682-1750, 2194-9034
Online Access:Get full text
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Summary:An important factor that reduced the accuracy of motion trajectories in existing VSLAM (Visual Simultaneous Localization and Mapping) systems is the poor estimation of the position pose of the vision odometer. The existing methods generate many incorrect matches during the feature matching process, resulting in low computational accuracy of rotations and translations between cameras, which further leads to a reduction in the robustness of the overall system. In addition, the sparse feature point maps do not provide a detailed description of the surrounding environment, which makes it difficult for the devices equipped with VSLAM systems to perform advanced tasks such as navigation, path planning and human-computer interaction. To address the accuracy problem, we select the set of matches from existing feature matching algorithms based on the motion consistency constraint and use a random sampling consistency algorithm to obtain the best quality matches from the selected samples for computing the geometric transformation model and estimating the current pose. To address the problem of sparse map points, we use the depth information from the RGB-D or Stereo camera to build a dense map module to ensure that information about the surrounding environment is recorded as a point cloud, which provides data support for the implementation of advanced tasks of the device.
Bibliography:ObjectType-Article-1
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SourceType-Conference Papers & Proceedings-1
content type line 22
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLVIII-1-W2-2023-679-2023