macvo visuals, merge posts

Author: MsFriendly

_posts/2025-05-13-macvo.md

@@ -16,27 +16,28 @@ We are thrilled to announce that our paper **"[MAC-VO](https://mac-vo.github.io/
 This prestigious recognition at the IEEE International Conference on Robotics and Automation (ICRA) 2025 highlights the significant impact of our work in advancing stereo visual odometry through learning-based approaches. The dual awards underscore both the technical excellence and practical relevance of our research in the robotics community.
 
 
-<div style="display: flex; justify-content: space-around;">
-    <figure style="width: 48%;">
-        <img src="/img/posts/2025-07-04-macvo-bestpaper/bestconference.jpg" alt="Best Conference Paper Award" style="width: 100%;">
-        <figcaption style="text-align: center;">Best Conference Paper Award</figcaption>
-    </figure>
-    <figure style="width: 48%;">
-        <img src="/img/posts/2025-07-04-macvo-bestpaper/bestperception.jpg" alt="Best Paper Award on Robot Perception" style="width: 100%;">
-        <figcaption style="text-align: center;">Best Paper Award on Robot Perception</figcaption>
-    </figure>
-</div>
-
+<table>
+  <tr>
+    <td>
+      <img src="/img/posts/2025-07-04-macvo-bestpaper/bestconference.jpg" width="100%"><br>
+      Best Conference Paper Award
+    </td>
+    <td>
+      <img src="/img/posts/2025-07-04-macvo-bestpaper/bestperception.jpg" width="100%"><br>
+      Best Paper Award on Robot Perception
+    </td>
+  </tr>
+</table>
 
 
+## About MAC-VO
 
+MAC-VO introduces a novel metrics-aware covariance framework that significantly improves the accuracy and reliability of learning-based stereo visual odometry systems. Our model leverages learned uncertainty to filter out low-quality features, enhance keypoint selection, and thereby improve pose estimation accuracy. MAC-VO also enables dense mapping using only stereo input, without requiring bundle adjustment or multi-frame optimization. Our approach addresses key challenges in uncertainty quantification and performance optimization for autonomous navigation applications.
 
+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.
 
-## About MAC-VO
+More details and open-source code can be found on our **[Project Website](https://mac-vo.github.io)**.
 
-MAC-VO introduces a novel metrics-aware covariance framework that significantly improves the accuracy and reliability of learning-based stereo visual odometry systems. Our approach addresses key challenges in uncertainty quantification and performance optimization for autonomous navigation applications.
-
-- **Project Website**: [https://mac-vo.github.io](https://mac-vo.github.io)
 
 ## Conference Highlights
 
@@ -49,4 +50,6 @@ Check out our live demonstrations from the conference:
 
 Congratulations to all team members who contributed to this achievement! This award represents the culmination of dedicated research efforts and collaborative work across our team.
 
-We look forward to continuing our research in visual odometry and contributing to the advancement of autonomous robotics technology.
+We look forward to continuing our research in visual odometry and contributing to the advancement of autonomous robotics technology.
+
+<video src="/img/posts/2025-07-04-macvo-bestpaper/macvo_bestpaper_web.mp4" autoplay loop muted width="95%"></video>