From a5947f4aea068b6b78869af3e2ef725279ba2cf7 Mon Sep 17 00:00:00 2001 From: haleqiu Date: Tue, 22 Oct 2024 11:35:09 -0400 Subject: [PATCH] add macvo project page --- _posts/2024-10-11-macvo.md | 25 +++++++++++++++++++++++++ img/posts/2024-10-11-macvo/macvo.jpg | Bin 0 -> 5213690 bytes 2 files changed, 25 insertions(+) create mode 100644 _posts/2024-10-11-macvo.md create mode 100644 img/posts/2024-10-11-macvo/macvo.jpg diff --git a/_posts/2024-10-11-macvo.md b/_posts/2024-10-11-macvo.md new file mode 100644 index 000000000..fde2e4004 --- /dev/null +++ b/_posts/2024-10-11-macvo.md @@ -0,0 +1,25 @@ +--- +layout: post +title: "MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry" +date: 2024-10-11 12:00:01 +categories: research +description: "MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry" +author: "Yuheng Qiu" +published: true +redirect: "https://mac-vo.github.io/" + +show_sidebar: false +# slim_content_width: true +permalink: /airimu/ +image: /img/posts/2024-10-11-macvo/macvo.jpg +datatable: true +title_image: None +hero_image: /img/posts/2024-10-11-macvo/macvo.jpg +hero_height: is-large +remove_hero_title: true +menubar_toc: true + +tags: Learning, Perception, SLAM +--- + +We propose the MAC-VO, a novel learning-based stereo VO that leverages the learned metrics-aware matching uncertainty for dual purposes: selecting keypoint and weighing the residual in pose graph optimization. Compared to traditional geometric methods prioritizing texture-affluent features like edges, our keypoint selector employs the learned uncertainty to filter out the low-quality features based on global inconsistency. In contrast to the learning-based algorithms that model the scale-agnostic diagonal weight matrix for covariance, we design a metrics-aware covariance model to capture the spatial error during keypoint registration and the correlations between different axes. Integrating this covariance model into pose graph optimization enhances the robustness and reliability of pose estimation, particularly in challenging environments with varying illumination, feature density, and motion patterns. On public benchmark datasets, MAC-VO outperforms existing VO algorithms and even some SLAM algorithms in challenging environments. 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