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首页> 外文会议>International conference on advanced concepts for intelligent vision systems >Omnidirectional Localization in vSLAM with Uncertainty Propagation and Bayesian Regression
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Omnidirectional Localization in vSLAM with Uncertainty Propagation and Bayesian Regression

机译：vSLAM中具有不确定性传播和贝叶斯回归的全向定位

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摘要

This article presents a visual localization technique based solely on the use of omnidirectional images, within the framework of mobile robotics. The proposal makes use of the epipolar constraint, adapted to the omnidirectional reference, in order to deal with matching point detection, which ultimately determines a motion transformation for localizing the robot. The principal contributions lay on the propagation of the current uncertainty to the matching. Besides, a Bayesian regression technique is also implemented, in order te reinforce the robustness. As a result, we provide a reliable adaptive matching, which proves its stability and consistency against non-linear and dynamic effects affecting the image frame, and consequently the final application. In particular, the search for matching points is highly reduced, thus aiding in the search and avoiding false correspondes. The final outcome is reflected by real data experiments, which confirm the benefit of these contributions, and also test the suitability of the localization when it is embedded on a vSLAM application.
机译：本文提出了一种仅在移动机器人技术框架内基于全向图像使用的视觉定位技术。该提案利用了适用于全向参考的对极约束，以处理匹配点检测，该匹配点检测最终确定了用于对机器人进行定位的运动转换。主要贡献在于将当前不确定性传播到匹配中。此外，还采用了贝叶斯回归技术，以增强鲁棒性。结果，我们提供了可靠的自适应匹配，证明了其对影响图像帧的非线性和动态效果以及最终应用的稳定性和一致性。尤其是，大大减少了对匹配点的搜索，从而有助于搜索并避免错误的对应。最终的结果由真实的数据实验反映出来，这些实验证实了这些贡献的益处，并且还测试了将本地化嵌入vSLAM应用程序时的适用性。

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