macvo highlight post and video

Author: MsFriendly

_posts/2025-05-13-macvo.md

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+---
+layout: post
+title:  "MAC-VO Selected as ICRA 2025 Best Paper Award Finalist"
+date:   2025-05-12 10:44:07
+categories: highlights
+author: "Yuheng Qiu"
+published: true
+sidebar:  false
+permalink: /highlight-macvo/
+image: /img/posts/2025-05-13-macvo/macvo_1080.gif
+# hero_image: /img/posts/2025-04-10-rayfronts/rayfronts-teaser.gif
+---
+
+We are thrilled to announce that our paper "[MAC-VO](https://mac-vo.github.io/): Metrics-aware Covariance for Learning-based Stereo Visual Odometry", has been selected as a finalist for the Best Perception Paper Award at the upcoming IEEE International Conference on Robotics and Automation (ICRA) 2025.
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+MAC-VO introduces a novel stereo visual odometry framework that leverages learned uncertainty to enhance keypoint selection and pose graph optimization. Unlike traditional methods that priortize texture-rich features, we proposed a novel metrics-aware covariance model to capture spatial erros and inter-axis correlations, effectively filtering out low-quality features and improving pose estimation accuracy. MAC-VO also enables uncertainty-aware dense mapping without the need for bundle adjustment or multi-frame optimization, facilitating the future research on planning and decision making.
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+We conducted extensive evaluations on public benchmark datasets, such as VBR, EuRoC, and TartanAir, and our own collection, encompassing a wide range of environments, including indoor, outdoor, and scenarios with extreme lightining conditions. These tests demonstrate that MAC-VO outperforms existing visual odometry algorithms and even some SLAM systems in difficult scenarios.
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+We look forward to presenting our work at ICRA 2025 in Atlanta, Georgia, on Tuesday, May 20th at 4:30 PM inRoom 302. For more details, please visit our project [website](https://mac-vo.github.io/) and [paper](https://arxiv.org/abs/2409.09479).