update sut-mrs dataset on airlab

_posts/2024-03-08-subt-mrs.md

@@ -10,10 +10,10 @@ redirect: "https://superodometry.com/datasets"
 show_sidebar: false
 # slim_content_width: true
 permalink: /subt-mrs/
-image: /img/posts/2024-03-08-subt-mrs/first_page.pdf
+image: /img/posts/2024-03-08-subt-mrs/first_page.png
 datatable: true
 title_image: None
-hero_image: /img/posts/2024-03-08-subt-mrs/first_page.pdf
+hero_image: /img/posts/2024-03-08-subt-mrs/first_page.png
 hero_height: is-large
 remove_hero_title: false
 menubar_toc: false
@@ -23,7 +23,7 @@ tags: Learning, SLAM, Perception
 Simultaneous localization and mapping (SLAM) is a fundamental task for numerous applications such as autonomous navigation and exploration. Despite many SLAM datasets have been released, current SLAM solutions still struggle to have sustained and resilient performance. One major issue is the absence of high-quality datasets including diverse all-weather conditions and a reliable metric for assessing robustness. This limitation significantly restricts the scalability and generalizability of SLAM technologies, impacting their development, validation, and deployment.
 
 <figure>
- <img src="/img/posts/2024-03-08-subt-mrs/first_page.pdf" alt="" />
+ <img src="/img/posts/2024-03-08-subt-mrs/first_page.png" alt="" />
 </figure>
 
 To bridge this gap and push SLAM towards all-weather environments, we present an extremely challenging dataset, SubT-MRS, including scenarios featuring various sensor degradation, aggressive locomotions, and extreme-weather conditions. The SubT-MRS dataset comprises 3 years of data from the DARPA Subterranean (SubT) Challenge (2019-2021) and extends with an additional 2 years of diverse environments (2022-2023), containing mixed indoor and outdoor settings, including long corridors, off-road scenario, tunnels, caves, deserts, forests, and bushlands.