镜像环境中视觉 SLAM 方法的基准测试

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Peter Herbert, Jing Wu, Ze Ji, Yu-Kun Lai
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引用次数: 0

摘要

视觉同步定位和绘图(vSLAM)应用于室内和室外导航,经常会遇到复杂的视觉问题,尤其是镜面反射。镜面存在的影响(可见时间和镜面在画面中的平均大小)被认为会影响定位和绘图性能,使用直接技术的系统预计性能会更差。因此,我们收集了在镜面环境中记录的图像序列数据集 MirrEnv,用于评估现有代表性方法的性能。随着镜像持续时间的增加,RGBD ORB-SLAM3 和 BundleFusion 似乎显示出绝对轨迹误差的适度下降,而其余结果并未显示出明显的定位性能下降。事实证明,生成的网格图非常不准确,真实反射和虚拟反射在重建中发生碰撞。本文讨论了可能的误差来源和镜面环境下的鲁棒性,概述了验证和改进 vSLAM 在平面镜面下性能的未来方向。MirrEnv 数据集可在 https://doi.org/10.17035/d.2023.0292477898 上查阅。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Benchmarking visual SLAM methods in mirror environments

Benchmarking visual SLAM methods in mirror environments

Visual simultaneous localisation and mapping (vSLAM) finds applications for indoor and outdoor navigation that routinely subjects it to visual complexities, particularly mirror reflections. The effect of mirror presence (time visible and its average size in the frame) was hypothesised to impact localisation and mapping performance, with systems using direct techniques expected to perform worse. Thus, a dataset, MirrEnv, of image sequences recorded in mirror environments, was collected, and used to evaluate the performance of existing representative methods. RGBD ORB-SLAM3 and BundleFusion appear to show moderate degradation of absolute trajectory error with increasing mirror duration, whilst the remaining results did not show significantly degraded localisation performance. The mesh maps generated proved to be very inaccurate, with real and virtual reflections colliding in the reconstructions. A discussion is given of the likely sources of error and robustness in mirror environments, outlining future directions for validating and improving vSLAM performance in the presence of planar mirrors. The MirrEnv dataset is available at https://doi.org/10.17035/d.2023.0292477898.

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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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