update iros2023

MetaSLAM · Sep 15, 2023 · f7c697e · f7c697e
1 parent 599dd25
commit f7c697e
Showing 1 changed file with 31 additions and 4 deletions.
diff --git a/competitions/iros2023.md b/competitions/iros2023.md
@@ -16,7 +16,35 @@ hero_image: /img/web.gif
 
 **Sample Dataset and Evaluation Tools will be released at 5th August.**
 
-****
+## Schedule
+
+<table>
+  <tr>
+    <td><p align="center"><strong>Event</strong></p></td>
+    <td><p align="center"><strong>Date</strong></p></td>
+  </tr>
+  <tr>
+    <td><p align="center">Release Initial Dataset & Eval Tools</p></td>
+    <td>01 Aug 2023</td>
+  </tr>
+  <tr>
+    <td><p align="center">Release Final Competition Set</p></td>
+    <td>15 Sep 2023</td>
+  </tr>
+  <tr>
+    <td><p align="center">Submission Close</p></td>
+    <td>24 Sep 2023</td>
+  </tr>
+  <tr>
+    <td><p align="center">Winners Notified</p></td>
+    <td>25 Sep 2023</td>
+  </tr>
+  <tr>
+    <td><p align="center">Winners Presentations</p></td>
+    <td>01 Oct 2023</td>
+  </tr>
+</table>
+
 
 ## Competition Background
 
@@ -60,10 +88,9 @@ PCD
 The test set includes 6036 submaps. Each submap consists of points queried within 50m.
 
 ### Submission and Evaluation
-The goal of the competition is to evaluated the performance of place recognition method in overlapping areas of trajectories. ***Top1*** and ***Top5*** recall will be calculated and shown in AIcrowd. The final competition rankings are based on the ***Top1*** recall.
+The objective of this competition is to assess the effectiveness of place recognition methods in scenarios involving overlapping trajectory areas. We will evaluate participants' submissions using both **Top1** and **Top5** recall metrics, and the final competition rankings will be determined based on the **Top1** recall score.
 
-Participants please unzip all the files and convert all the point cloud into global descriptors in the same order. 
-The format of submission should be the standard binary file format in Numpy(.npy).
+We kindly request participants to unzip all provided files and convert the point cloud data into global descriptors while preserving the original order. For instance, the global descriptor for "1.pcd" should correspond to index 0, "2.pcd" to index 1, and so forth. Submissions should be the standard binary file format using Numpy (.npy).
 
 ## Sample Dataset