diff --git a/README.md b/README.md
index 8c23ed42..b07c0549 100644
--- a/README.md
+++ b/README.md
@@ -1,100 +1,87 @@
-Fast Traversability Estimation for Wild Visual Navigation
+
+
+ Wild Visual Navigation
+
+
- Citation •
- Installation •
+ Installation •
Overview •
Experiments •
- Development
-
- 
+ Development •
+ Citation
-
-
-
-
-## Citation
-```
-@INPROCEEDINGS{frey23fast,
- AUTHOR = {Jonas, Frey and Matias, Mattamala and Nived, Chebrolu and Cesar, Cadena and Maurice, Fallon and Marco, Hutter},
- TITLE = {\href{https://arxiv.org/abs/2305.08510}{Fast Traversability Estimation for Wild Visual Navigation}},
- BOOKTITLE = {Proceedings of Robotics: Science and Systems},
- YEAR = {2023},
- ADDRESS = {Daegu, Republic of Korea},
- MONTH = {July},
- DOI = {10.15607/RSS.2023.XIX.054}
-}
-```
-If you are also building up on the STEGO integration or using the pre-trained models for a comparision please cite:
-```
-@INPROCEEDINGS{mattamala24wild,
- AUTHOR = {Mattamala, Matias and Jonas, Frey and Piotr Libera and Chebrolu, Nived and Georg Martius and Cadena, Cesar and Hutter, Marco and Fallon, Maurice},
- TITLE = {{Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision}},
- BOOKTITLE = {under review for Autonomous Robots},
- YEAR = {2024}
-}
-```
+
+
+
+ 
+ 
+
+
-If you are using the elevation_mapping integration
-```
-@INPROCEEDINGS{erni23mem,
- AUTHOR={Erni, Gian and Frey, Jonas and Miki, Takahiro and Mattamala, Matias and Hutter, Marco},
- TITLE={\href{https://arxiv.org/abs/2309.16818}{MEM: Multi-Modal Elevation Mapping for Robotics and Learning}},
- BOOKTITLE={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
- YEAR={2023},
- PAGES={11011-11018},
- DOI={10.1109/IROS55552.2023.10342108}
-}
-```
+
-Checkout out also our other works.
## Installation
-1. Clone the WVN and our STEGO reimplementation.
+### Minimal
+Clone the WVN and our STEGO reimplementation.
```shell
mkdir ~/git && cd ~/git
git clone git@github.com:leggedrobotics/wild_visual_navigation.git
git clone git@github.com:leggedrobotics/self_supervised_segmentation.git
```
-2. Install the virtual environment.
+(Recommended) Create new virtual environment.
```shell
-cd ~/git/wild_visual_navigation
-# TODO
+mkdir ~/.venv
+python -m venv ~/venv/wvn
+source ~/venv/wvn/bin/activate
```
-3. Install the wild_visual_navigation package.
+Install the wild_visual_navigation package.
```shell
cd ~/git
pip3 install -e ./wild_visual_navigation
+pip3 install -e ./self_supervised_segmentation
```
-4. [Optionally] Configure custom paths
-Set your custom global paths by defining the ENV_WORKSTATION_NAME and exporting the variable in your `~/.bashrc`.
-
- ```shell
- export ENV_WORKSTATION_NAME=your_workstation_name
- ```
-The paths can be specified by modifying `wild_visual_navigation/wild_visual_navigation/cfg/gloabl_params.py`.
-Add your desired global paths.
-Per default, all results are stored in `wild_visual_navigation/results`.
-
-
-
## Overview
-
-What we provide:
-- Learning and Feature Extraction Nodes integrated in ROS1
-- Gazebo Test Simulation Envrionment
-- Example ROSbags
-- Pre-trained models with minimalistic inference script (can be used as a easy baseline)
+
+### Repository Structure
+```
+📦wild_visual_navigation
+ ┣ 📂assets
+ ┣ 📂demo_data # Example images
+ ┣ 🖼 example_images.png
+ ┗ ....
+ ┗ 📂checkpoints # Pre-trained model checkpoints
+ ┣ 📜 mountain_bike_trail_v2.pt
+ ┗ ....
+ ┣ 📂docker # Quick start docker container
+ ┣ 📂results
+ ┣ 📂test
+ ┣ 📂wild_visual_navigation # Core implementation of WVN
+ ┣ 📂wild_visual_navigation_anymal # ROS1 ANYmal helper package
+ ┣ 📂wild_visual_navigation_jackal # ROS1 Jackal simulation example
+ ┣ 📂wild_visual_navigation_msgs # ROS1 message definitions
+ ┣ 📂wild_visual_navigation_ros # ROS1 nodes for running WVN
+ ┗ 📂scripts
+ ┗ 📜 wvn_feature_extractor_node.py
+ ┗ 📜 wvn_learning_node.py
+ ┗ 📜 quick_start.py # Inferencing demo_data from pre-trained checkpoints
+```
+### Features
+- quick_start script for inference using pre-trained models (can be used as an easy baseline)
+- ROS1 integration for online deployment
+- Jackal Gazebo simulation demo integration
+- Docker container for easy installation
- Integration into elevation_mapping_cupy
@@ -102,43 +89,52 @@ What we provide:
## Experiments
-### Inference pre-trained model
+### Inference of pre-trained model
+Script to inference traversability of images within input folder (`assets/demo_data/*.png`), given a pre-trained model checkpoint (`assets/checkpoints/model_name.pt`). The script stores the result in the provided output folder (`results/demo_data/*.png`).
+```python
+python3 quick_start.py
-### Online adaptation [Simulation]
+# python3 quick_start.py --help for more CLI information
+# usage: quick_start.py [-h] [--model_name MODEL_NAME] [--input_image_folder INPUT_IMAGE_FOLDER]
+# [--output_folder_name OUTPUT_FOLDER_NAME] [--network_input_image_height NETWORK_INPUT_IMAGE_HEIGHT]
+# [--network_input_image_width NETWORK_INPUT_IMAGE_WIDTH] [--segmentation_type {slic,grid,random,stego}]
+# [--feature_type {dino,dinov2,stego}] [--dino_patch_size {8,16}] [--dino_backbone {vit_small}]
+# [--slic_num_components SLIC_NUM_COMPONENTS] [--compute_confidence] [--no-compute_confidence]
+# [--prediction_per_pixel] [--no-prediction_per_pixel]
+```
+### Online adaptation [Simulation]
+Instructions can be found within [wild_visual_navigation_jackal/README.md](wild_visual_navigation_jackal/README.md).
### Online adaptation [Rosbag]
+#### Download Rosbags:
+To quickly test out online training and adaption we provide some example rosbags ( [GDrive](https://drive.google.com/drive/folders/1Rf2TRPT6auFxOpnV9-ZfVMjmsvdsrSD3?usp=sharing) ), collected with our ANYmal D robot.
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-
-
+#### Example Result:
+
| MPI Outdoor | MPI Indoor | Bahnhofstrasse | Bike Trail |
|----------------|------------|-------------|---------------------|
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