From c42b25efa40225e1fac0be697d30be95d8d041ff Mon Sep 17 00:00:00 2001 From: Kin-home Date: Thu, 18 Apr 2024 21:52:29 +0200 Subject: [PATCH] docs(web): update website arXiv link --- index.html | 104 ++++++++++++------------------- resources/elements/utils/zoom.js | 7 +-- 2 files changed, 43 insertions(+), 68 deletions(-) diff --git a/index.html b/index.html index ab79cbeb..ab9cefbb 100644 --- a/index.html +++ b/index.html @@ -37,9 +37,9 @@

  • - + Daniel Duberg* 1 - +
  • @@ -63,9 +63,8 @@

    - *Equal contribution -
    - 1KTH Royal Institute of Technology, 2HKUST + *Co-first authors 1KTH Royal Institute of Technology  2HKUST +

    @@ -75,17 +74,17 @@

    @@ -116,7 +115,7 @@

    Abstract

    - The dynamic nature of the real world is one of the main challenges in robotics. The first step in dealing with it is to detect which parts of the world are dynamic. A typical benchmark task is to create a map that contains only the static part of the world to support, for example, localization and planning. Current solutions are often applied in post-processing, where parameter tuning allows the user to adjust the setting for a specific dataset. In this paper, we propose DUFOMap, a novel dynamic awareness mapping framework designed for efficient online processing. Despite having the same parameter settings for all scenarios, it performs better or is on par with state-of-the-art methods. Ray casting is utilized to identify and classify fully observed empty regions. Since these regions have been observed empty, it follows that anything inside them at another time must be dynamic. Evaluation is carried out in various scenarios, including outdoor environments in KITTI and Argoverse 2, open areas on the KTH campus, and with different sensor types. DUFOMap outperforms the state of the art in terms of accuracy and computational efficiency. The source code, benchmarks, and links to the datasets utilized are provided. + The dynamic nature of the real world is one of the main challenges in robotics. The first step in dealing with it is to detect which parts of the world are dynamic. A typical benchmark task is to create a map that contains only the static part of the world to support, for example, localization and planning. Current solutions are often applied in post-processing, where parameter tuning allows the user to adjust the setting for a specific dataset. In this paper, we propose DUFOMap, a novel dynamic awareness mapping framework designed for efficient online processing. Despite having the same parameter settings for all scenarios, it performs better or is on par with state-of-the-art methods. Ray casting is utilized to identify and classify fully observed empty regions. Since these regions have been observed empty, it follows that anything inside them at another time must be dynamic. Evaluation is carried out in various scenarios, including outdoor environments in KITTI and Argoverse 2, open areas on the KTH campus, and with different sensor types. DUFOMap outperforms the state of the art in terms of accuracy and computational efficiency.

    +

    Section I-C: DUFOMap Ablation Study in RGB-D dataset @@ -281,41 +263,36 @@

    -

    @@ -326,8 +303,7 @@


--> diff --git a/resources/elements/utils/zoom.js b/resources/elements/utils/zoom.js index 48e1cad2..46577341 100644 --- a/resources/elements/utils/zoom.js +++ b/resources/elements/utils/zoom.js @@ -1,7 +1,6 @@ -$(document).ready( -function(){ +$(document).ready(function(){ mediumZoom('[data-zoomable]', { background: 'rgba(0, 0, 0, 0.5)' // 50% alpha transparency - });} -); + }); +});