From 19f683e965f4cac6f6c2f610cedc598d47a778bc Mon Sep 17 00:00:00 2001 From: matlabbe Date: Wed, 29 Jun 2022 22:45:41 -0400 Subject: [PATCH] Update index.md --- index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.md b/index.md index 6da4e80c..aae0b6d2 100644 --- a/index.md +++ b/index.md @@ -69,7 +69,7 @@ This website uses Google Analytics. See their Privacy Policy [here](https://supp ## What's new ### June 2022 -A new paper has been published: **Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments**. The general idea is to remap multiple times the same environment to capture multiple illumination variations caused by natural and artificial lighting, then the robot would be able to localize afterwards at any hour of the day. For more details, see this [page](https://github.com/introlab/rtabmap/tree/master/archive/2022-IlluminationInvariant) and the linked paper. Some great comparisons about robustness to illumination variations between binary descriptions (BRIEF/ORB, BRISK), float descriptions (SURF/SIFT/KAZE/DAISY) and learned descriptors (SuperPoint). +A new paper has been published: **Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments**. The general idea is to remap multiple times the same environment to capture multiple illumination variations caused by natural and artificial lighting, then the robot would be able to localize afterwards at any hour of the day. For more details, see this [page](https://github.com/introlab/rtabmap/tree/master/archive/2022-IlluminationInvariant) and the linked paper. Some great comparisons about robustness to illumination variations between binary descriptors (BRIEF/ORB, BRISK), float descriptors (SURF/SIFT/KAZE/DAISY) and learned descriptors (SuperPoint). ![Illumination-invariant](https://github.com/introlab/rtabmap/raw/master/archive/2022-IlluminationInvariant/images/fig_overview.jpg) ### January 2022