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@@ -1,9 +1,20 @@
-# 2024-tfm-rebeca-villaraso
+# TFM 2024 URJC Visión Artificial - Rebeca Villarraso
 
-## Acknowledgments
-@article{goose-dataset,
-    author = {Peter Mortimer and Raphael Hagmanns and Miguel Granero
-              and Thorsten Luettel and Janko Petereit and Hans-Joachim Wuensche},
-    title = {The GOOSE Dataset for Perception in Unstructured Environments},
-    url={https://arxiv.org/abs/2310.16788},
-    year = 2023
+# Week 0
+- TFM proposal study: development of a robot's perception in unstructured environments.
+- Work planning:
+  1. Contact Goose dataset developers to find out data availability. Study dataset structure.
+  2. Obtain RELLIS-3D dataset and know its structure.
+  3. Develop semantic segmentation algorithm for testing.
+  4. Study other data sets (RUGD, FIRE, CITYSCAPES).
+ 
+# Week 1
+- DeepLabV3+ semantic segmentation with "instance-level_human_parsing" dataset.
+  (DeepLabV3+ is a ResNet50 pretrained model variation based on enconder-decoder blocks).
+- RELLIS-3D (a Multi-modal Dataset for Off-Road Robotics) (images and LIDAR) obtained and preprocessed from: https://gamma.umd.edu/publication.
+- Reproduction of Ga-Nav Sematic Segmentation of Rellis-3D Images according to: https://github.com/unmannedlab/RELLIS-3D
+- On going: adaptation the DeepLabV3+ model to train RELLIS-3D dataset.
+- Work planning:
+  1. Continue adapting DeepLabV3+ with the RELLIS-3D and RUGD datasets.
+  2. GitHub Pages.
+  3. Study the metrics and models proposed on the Cityscapes dataset website: https://www.cityscapes-dataset.com/benchmarks/