readme fixes

Signed-off-by: pepisg <pedro.gonzalez@eia.edu.co>

Author: pepisg

tutorials/docs/navigation2_with_semantic_segmentation.rst

@@ -33,6 +33,7 @@ It is assumed ROS2 and Nav2 dependent packages are installed or built locally. A
 You will also need to compile the semantic_segmentation_layer package. To do it, clone the repo to your ros2 workspace source, checkout to the appropriate branch and build the package:
 
 .. code-block:: bash
+
    # on your workspace source replace rolling with your ros distro. branches are available for humble, jazzy and rolling.
    git clone -b rolling https://github.com/kiwicampus/semantic_segmentation_layer.git
    cd <your workspace path>
@@ -66,29 +67,30 @@ However, if you want to train your own model, you can use the `Simple Segmentati
     :align: center
     :alt: Example of semantic segmentation showing original image and segmented mask
 
-Once trained, The output of a semantic segmentation model is typically an image with the same size as the input, where each pixel holds the probability of that pixel belonging to each class. 
-For instance, in the model provided in this tutorial has 3 classes: sidewalk, grass, and background; hence its raw output is a 3-channel image, where each channel corresponds to the probability of the pixel belonging to each class.
+Once trained, the output of a semantic segmentation model is typically an image with the same size as the input, where each pixel holds the probability of that pixel belonging to each class. 
+For instance, the model provided in this tutorial has 3 classes: sidewalk, grass, and background; hence its raw output is a 3-channel image, where each channel corresponds to the probability of the pixel belonging to that class.
 At the end, the class with the highest probability is selected for each pixel, and a confidence value is calculated as the probability of the class that was selected. 
 
-A perfectly working model should have a confidence value of 1 for the class that was selected, and 0 for the other classes, however this is rarely the case. Pixels with lower confidence usually correspond to classifications that may be wrong, 
-for that reason both the class and the confidence are important inputs for deciding how to assign a cost to a pixel, and both are taken into account by the semantic segmentation layer. You can refer to its `README <https://github.com/kiwicampus/semantic_segmentation_layer>`_ for a detailed explanation on how this is done
+A perfectly working model should have a confidence value of 1 for the class that was selected, and 0 for the other classes; however, this is rarely the case. Pixels with lower confidence usually correspond to classifications that may be wrong. 
+For that reason, both the class and the confidence are important inputs for deciding how to assign a cost to a pixel, and both are taken into account by the semantic segmentation layer. You can refer to its `README <https://github.com/kiwicampus/semantic_segmentation_layer>`_ for a detailed explanation on how this is done.
 
 
 Tutorial Steps
 ==============
 
 0- Setup Simulation Environment
-------------------------------
+-------------------------------
 
 To navigate using semantic segmentation, we first need to set up a simulation environment with a robot equipped with a camera sensor. For this tutorial, we will use the Baylands outdoor world in Gazebo with a TurtleBot 4 robot.
 Everything is already set up in the `nav2_semantic_segmentation_demo <https://github.com/ros-navigation/navigation2_tutorials/tree/master/nav2_semantic_segmentation_demo>`_ package, so clone the repo and build it if you haven't already:
 
-
 .. code-block:: bash
-   # on your workspace source folder
-   git clone https://github.com/navigation2-tutorials/navigation2_tutorials.git
+
+   # On your workspace source folder
+   git clone https://github.com/ros-navigation/navigation2_tutorials.git
    cd <your workspace path>
    colcon build --symlink-install --packages-up-to nav2_semantic_segmentation_demo
+
    source install/setup.bash
 
 Test that the simulation launches correctly:
@@ -108,7 +110,7 @@ You should see Gazebo launch with the TurtleBot 4 in the Baylands world.
 -----------------------------------------------
 
 The semantic segmentation node performs real-time inference on camera images using an ONNX model. It subscribes to camera images, runs inference, and publishes segmentation masks, confidence maps, and label information.
-To run the semantic segmentation node, you need to install the `requirements.txt <https://github.com/ros-navigation/navigation2_tutorials/blob/master/nav2_semantic_segmentation_demo/semantic_segmentation_node/requirements.txt>`_ file in the semantic_segmentation_node package: 
+To run the semantic segmentation node, you need to install the dependencies from the `requirements.txt <https://github.com/ros-navigation/navigation2_tutorials/blob/master/nav2_semantic_segmentation_demo/semantic_segmentation_node/requirements.txt>`_ file in the semantic_segmentation_node package: 
 
 .. code-block:: bash
 
@@ -117,8 +119,8 @@ To run the semantic segmentation node, you need to install the `requirements.txt
 
 The segmentation node is configured through an ontology YAML file that defines:
 
-- **Classes to detect**: Each class has a name, text prompt (for certain model types), and color for visualization. classes should be defined in the same order as the model output. 0 is always the background class.
-- **Model settings**: Device (CPU/CUDA), image preprocessing parameters. we use the CPU for inference for greater compatibility, however if you have a GPU you can install onnxruntime-gpu and set the device to cuda.
+- **Classes to detect**: Each class has a name and color for visualization. Classes should be defined in the same order as the model output. 0 is always the background class.
+- **Model settings**: Device (CPU/CUDA), image preprocessing parameters. We use the CPU for inference for greater compatibility; however, if you have a GPU you can install onnxruntime-gpu and set the device to cuda.
 
 An example configuration file (`config/ontology.yaml`):
 
@@ -127,17 +129,12 @@ An example configuration file (`config/ontology.yaml`):
    ontology:
      classes:
        - name: sidewalk
-         prompt: sidewalk
          color: [255, 0, 0]  # BGR format
        - name: grass
-         prompt: grass
          color: [0, 255, 0]  # BGR format
    
    model:
      device: cpu  # cuda or cpu
-     max_image_dim: 1024
-     mask_opacity: 0.15
-     border_width: 1
 
 The node publishes several topics:
 
@@ -216,7 +213,7 @@ The tutorial provides a complete launch file that launches the simulation, the s
 
    ros2 launch semantic_segmentation_sim segmentation_simulation_launch.py
 
-The baylands simulation and `rviz` should appear. You should be able to send navigation goals via `rviz` and the robot should navigate the Baylands world, preferring sidewalks and avoiding grass:
+The Baylands simulation and `rviz` should appear. You should be able to send navigation goals via `rviz` and the robot should navigate the Baylands world, preferring sidewalks and avoiding grass:
 
 .. image:: images/Navigation2_with_segmentation/demo.gif
     :width: 90%
@@ -234,7 +231,7 @@ Again, you can refer to the picture on the Layer's `README <https://github.com/k
 Conclusion
 ==========
 
-This tutorial demonstrated how to integrate semantic segmentation with Nav2 for terrain-aware navigation. using a pretrained model that works on gazebo's Baylands world and a custom semantic segmentation layer plugin.
+This tutorial demonstrated how to integrate semantic segmentation with Nav2 for terrain-aware navigation using a pretrained model that works on Gazebo's Baylands world and a custom semantic segmentation layer plugin.
 
 To go further, you can train your own model using the `Simple Segmentation Toolkit <https://github.com/pepisg/simple_segmentation_toolkit>`_, and tune the costmap parameters to your own application.