Merge pull request #2 from NVIDIA-ISAAC-ROS/hotfix-2.0.0-1

Add latest blog posts and update documentation

public/.buildinfo

@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 61fccc13459316a96c72348ee501275b
+tags: 645f666f9bcd5a90fca523b33c5a78b7

public/_sources/blog/index.rst.txt

@@ -25,6 +25,8 @@ Isaac ROS Webinar Series
 NVIDIA Developer Blogs
 ----------------------
 
+-  `Accelerate AI-Enabled Robotics with Advanced Simulation and Perception Tools on NVIDIA Isaac 
+   Platform <https://developer.nvidia.com/blog/accelerate-ai-enabled-robotics-with-advanced-simulation-and-perception-tools-in-nvidia-isaac-platform/>`__
 -  `Open-Source Fleet Management Tools for Autonomous Mobile
    Robots <https://developer.nvidia.com/blog/open-source-fleet-management-tools-for-autonomous-mobile-robots/>`__
 -  `Improve Perception Performance for ROS 2 Applications with NVIDIA
@@ -47,6 +49,10 @@ NVIDIA Developer Blogs
 ROS Discourse
 -------------
 
+-  `Isaac ROS 2.0 release at ROSCon - 2 years of accelerating
+   ROS <https://discourse.ros.org/t/isaac-ros-2-0-release-at-roscon-2-years-of-accelerating-ros/34234>`__
+-  `Nova Carter AMR for ROS 2 w/ 800 megapixel/sec sensor
+   processing <https://discourse.ros.org/t/nova-carter-amr-for-ros-2-w-800-megapixel-sec-sensor-processing/34215>`__
 -  `Isaac ROS May update, cuVSLAM, AI stereo depth, and improved
    performance <https://discourse.ros.org/t/isaac-ros-may-update-cuvslam-ai-stereo-depth-and-improved-performance/31635/1>`__
 -  `Hyperdrive robotics applications with ros2_benchmark

public/_sources/concepts/compression/h264/tutorial_compatible_decode.rst.txt

@@ -9,17 +9,17 @@ Decoding Jetson H.264 images on non-NVIDIA powered systems
 Overview
 ------------
 
-Using hardware-accelerated Isaac ROS compression on Jetson to H.264
+Using hardware-accelerated Isaac ROS compression to H.264
 encode data for playback through Isaac ROS H.264 decoder on
-NVIDIA-powered x86_64 is fast and efficient. However, you may have need
-to decode recorded data on x86_64 systems that are not NVIDIA-powered.
+NVIDIA-powered systems is fast and efficient. However, you may have need
+to decode recorded data on systems that are not NVIDIA-powered.
 Fortunately, the output of the ``isaac_ros_h264_encoder`` package can
 easily be reformatted to work with other available ROS 2 decoder
 packages.
 
 This tutorial shows you how to replay a rosbag recorded on a Jetson
 using a third-party H.264 decoder `image_transport
-plugin <https://github.com/KDharmarajanDev/h264_image_transport>`__
+plugin <https://github.com/clydemcqueen/h264_image_transport>`__
 used in `FogROS2 <https://github.com/berkeleyAutomation/FogROS2>`__.
 
 The launch file here replays a target rosbag recorded with
@@ -36,7 +36,7 @@ Tutorial Walkthrough
    .. code:: bash
 
       cd ~/workspaces/isaac_ros-dev/src
-      git clone https://github.com/KDharmarajanDev/h264_image_transport.git
+      git clone https://github.com/clydemcqueen/h264_image_transport.git
 
       # Install dependencies for the third-party package
       sudo apt install libavdevice-dev libavformat-dev libavcodec-dev libavutil-dev libswscale-dev

public/_sources/concepts/compression/h264/tutorial_nitros_graph.rst.txt

@@ -13,8 +13,6 @@ using a :ir_repo:`Argus-compatible camera <isaac_ros_argus_camera>`
 and :ir_repo:`isaac_ros_h264_encoder <isaac_ros_compression>`,
 and save the compressed images into a rosbag.
 
-   Note: ``isaac_ros_h264_encoder`` needs to run on Jetson platform.
-
 1. Follow the quickstart :ref:`here <repositories_and_packages/isaac_ros_compression/isaac_ros_h264_decoder/index:quickstart>` up to
    step 6.
 

public/_sources/concepts/compression/h264/tutorial_realsense_encoder.rst.txt

@@ -9,11 +9,6 @@ camera and
 :ir_repo:`isaac_ros_h264_encoder <isaac_ros_compression> <isaac_ros_h264_encoder/src/encoder_node.cpp>`,
 and save the compressed images into a rosbag.
 
-.. note::
-
-   ``isaac_ros_h264_encoder`` needs to run on a Jetson
-   platform.
-
 .. note::
 
    This tutorial requires a compatible RealSense

public/_sources/concepts/missions/isaac_ros_mission_client.rst.txt

@@ -17,7 +17,7 @@ Tutorial with Isaac Sim
    :alt: Tutorial Visual
    :align: center
 
-This tutorial walks you through steps to send missions from `Mission Dispatch <https://gitlab-master.nvidia.com/isaac_amr_platform/mission_dispatch>`_ to Isaac ROS Mission Client using the `VDA5050 protocol <https://github.com/VDA5050/VDA5050/blob/main/VDA5050_EN.md>`_. The Mission Client is attached to an Isaac Sim environment running the `Nav2 stack <https://navigation.ros.org/>`_. The summary of steps are as follows:
+This tutorial walks you through steps to send missions from `Mission Dispatch <https://github.com/nvidia-isaac/isaac_mission_dispatch>`_ to Isaac ROS Mission Client using the `VDA5050 protocol <https://github.com/VDA5050/VDA5050/blob/main/VDA5050_EN.md>`_. The Mission Client is attached to an Isaac Sim environment running the `Nav2 stack <https://navigation.ros.org/>`_. The summary of steps are as follows:
 
 1. Set up Isaac Sim
 2. Run Mission Client
@@ -113,14 +113,14 @@ Tutorial Walkthrough
       Start the API and database server with the official Docker container.
 
       .. code-block:: bash
-
-          docker run -it --network host nvcr.io/nvstaging/isaac/mission-database:isaac_ros
+        
+          docker run -it --network host nvcr.io/nvidia/isaac/mission-database:isaac_ros
 
       To see what configuration options are, run:
 
       .. code-block:: bash
 
-          docker run -it --network host nvcr.io/nvstaging/isaac/mission-database:isaac_ros --help
+          docker run -it --network host nvcr.io/nvidia/isaac/mission-database:isaac_ros --help
 
       # For example, if you want to change the port for the user API from the default 5000 to 5002, add `--port 5002` configuration option in the command.
 
@@ -130,13 +130,13 @@ Tutorial Walkthrough
 
       .. code-block:: bash
 
-          docker run -it --network host nvcr.io/nvstaging/isaac/mission-dispatch:isaac_ros
+          docker run -it --network host nvcr.io/nvidia/isaac/mission-dispatch:isaac_ros
 
       # To see what the configuration options are, add --help option after the command.
 
 .. note::
 
-    Read `this tutorial <https://gitlab-master.nvidia.com/isaac_amr_platform/mission_dispatch#getting-started-with-deployment-recommended>`_ for more deployment options for Mission Dispatch.
+    Read `this tutorial <https://github.com/nvidia-isaac/isaac_mission_dispatch#getting-started-with-deployment-recommended>`_ for more deployment options for Mission Dispatch.
 
 12. Open `http://localhost:5000/docs` in a web browser.
 13. Use the `POST /robots` endpoint to create robot objects. See the video below for steps.

public/_sources/concepts/scene_reconstruction/bi3d_freespace_segmentation/tutorial_isaac_sim.rst.txt

@@ -28,7 +28,7 @@ Tutorial Walkthrough
         :width: 500px
         :align: center
 
-4.  Press **Play** to start publishing data from the Isaac Sim.
+4.  Press **Play** to start publishing data from Isaac Sim.
 
     .. figure:: :ir_lfs:`<resources/isaac_ros_docs/getting_started/isaac_sim_sample_scene.png>`
         :align: center
@@ -58,7 +58,7 @@ Tutorial Walkthrough
         :align: center
 
 7.  Optionally, you can run the visualizer script to visualize the
-    disparity image.
+    disparity image:
 
     .. code:: bash
 

public/_sources/concepts/scene_reconstruction/bi3d_freespace_segmentation/tutorial_realsense.rst.txt

@@ -17,14 +17,14 @@ to create a local occupancy grid.
 .. note::
 
    This tutorial requires a compatible RealSense camera from
-   the list available
-   :ref:`here <getting_started/hardware_setup/sensors/realsense_setup:Camera Compatibility>`.
+   the list of available
+   :ref:`cameras <getting_started/hardware_setup/sensors/realsense_setup:Camera Compatibility>`.
 
 Tutorial Walkthrough
 --------------------
 
 1.  Complete the :doc:`RealSense setup tutorial </getting_started/hardware_setup/sensors/realsense_setup>`.
-2.  Complete steps 1-7 described in the 
+2.  Complete steps 1-7 described in the
     :ref:`Quickstart Guide <repositories_and_packages/isaac_ros_freespace_segmentation/isaac_ros_bi3d_freespace/index:quickstart>`.
 3.  Open a new terminal and launch the Docker container using the
     ``run_dev.sh`` script:
@@ -42,18 +42,18 @@ Tutorial Walkthrough
          colcon build --symlink-install && \
          source install/setup.bash
 
-5.  Please set your camera as shown in the image below, which is on a
+5.  Set your camera as shown in the image below, which is on a
     tripod ~10cm tall and parallel to the ground. Or you can change the
-    static transform in launch file
+    static transform in the launch file
     :ir_repo:`here <isaac_ros_freespace_segmentation> <isaac_ros_bi3d_freespace/launch/isaac_ros_bi3d_freespace_realsense.launch.py#L144-157>`,
-    according to the placement of your camera with respect to a
+    according to the placement of your camera with respect to an
     occupancy grid origin frame.
 
    .. figure:: :ir_lfs:`<resources/isaac_ros_docs/repositories_and_packages/isaac_ros_freespace_segmentation/realsense_camera_position.jpg>`
       :width: 400px
       :align: center
 
-6.  Run the launch file, which launches the example:
+6.  Run the launch file, to launch the example:
 
     .. code:: bash
 
@@ -69,7 +69,7 @@ Tutorial Walkthrough
        ./scripts/run_dev.sh
 
 8.  Optionally, you can run the visualizer script to visualize the
-    disparity image.
+    disparity image:
 
     .. code:: bash
 
@@ -95,7 +95,7 @@ Tutorial Walkthrough
        cd ~/workspaces/isaac_ros-dev/src/isaac_ros_common && \
        ./scripts/run_dev.sh
 
-10. Visualize the occupancy grid in RViz.
+10. Visualize the occupancy grid in RViz:
 
     Start RViz:
 
@@ -105,6 +105,6 @@ Tutorial Walkthrough
 
     In the left pane, change the **Fixed Frame** to ``base_link``.
 
-    In the left pane, click the **Add** button, then select **By topic**
+    In the left pane, click **Add**, select **By topic**
     followed by **Map** to add the occupancy grid. You should see an
     output similar to the one shown at the top of this page.

public/_sources/concepts/scene_reconstruction/bi3d_freespace_segmentation/tutorial_zed.rst.txt

@@ -13,19 +13,19 @@ This tutorial demonstrates how to use a
 :ir_repo:`isaac_ros_bi3d_freespace <isaac_ros_freespace_segmentation> <isaac_ros_bi3d_freespace>`
 to create a local occupancy grid.
 
-.. note:: 
+.. note::
 
    This tutorial requires a compatible ZED camera from the
-   list available
-   :ref:`here <getting_started/hardware_setup/sensors/zed_setup:camera compatibility>`.
+   list of available
+   :ref:`cameras <getting_started/hardware_setup/sensors/zed_setup:camera compatibility>`.
 
 Tutorial Walkthrough
 --------------------
 
-1. Complete the 
+1. Complete the
    :doc:`ZED setup tutorial </getting_started/hardware_setup/sensors/zed_setup>`.
 2. Complete steps 1-7 described in the
-   :ref:`Quickstart Guide <repositories_and_packages/isaac_ros_freespace_segmentation/isaac_ros_bi3d_freespace/index:quickstart>`.
+   :ref:`Freespace Quickstart <repositories_and_packages/isaac_ros_freespace_segmentation/isaac_ros_bi3d_freespace/index:quickstart>`.
 3. Open a new terminal and launch the Docker container using the
    ``run_dev.sh`` script:
 
@@ -42,23 +42,23 @@ Tutorial Walkthrough
         colcon build --symlink-install && \
         source install/setup.bash
 
-5. Please set your camera as shown in the image below, which is on a
+5. Set your camera as shown in the image below, which is on a
    tripod ~5cm tall and parallel to the ground. Or you can change the
-   static transform in launch file
+   static transform in the launch file
    :ir_repo:`here <isaac_ros_freespace_segmentation> <isaac_ros_bi3d_freespace/launch/isaac_ros_bi3d_freespace_zed.launch.py#L144-157>`,
-   according to the placement of your camera with respect to a occupancy
+   according to the placement of your camera with respect to an occupancy
    grid origin frame.
 
    .. figure:: :ir_lfs:`<resources/isaac_ros_docs/repositories_and_packages/isaac_ros_freespace_segmentation/zed_bi3d_freespace_position.jpg>`
       :width: 400px
       :align: center
 
       **Note**: If you are not using the ZED2i camera, modify the
-      ``camera_model`` variable in the 
+      ``camera_model`` variable in the
       :ir_repo:`launch file <isaac_ros_freespace_segmentation> <isaac_ros_bi3d_freespace/launch/isaac_ros_bi3d_freespace_zed.launch.py>`
-      to ``zed``, ``zed2``, ``zed2i``, ``zedm``, ``zedx`` or ``zedxm``.
+      to ``zed``, ``zed2``, ``zed2i``, ``zedm``, ``zedx``, or ``zedxm``.
 
-6. Run the launch file, which launches the example:
+6. Run the launch file, to launch the example:
 
    .. code:: bash
 

public/_sources/concepts/scene_reconstruction/index.rst.txt

@@ -2,7 +2,7 @@
 Scene Reconstruction
 ====================
 
-3D scene reconstruction is a technique used in robotics that involves creating a detailed three-dimensional representation of the robot's surroundings, which
+3D scene reconstruction is a technique used in robotics that involves creating a detailed three-dimensional representation of the robot's surroundings that
 can be used for various purposes such as navigation, object recognition, and manipulation.
 
 

public/_sources/concepts/scene_reconstruction/nvblox/index.rst.txt

@@ -7,36 +7,32 @@ Nvblox
 
 Nvblox is a library for reconstruction and mapping targeted at
 robotics applications. It builds a voxel-based map from depth-images and/or
-3D LiDAR scans. The library outputs a mesh of the world in real-time,
-as well as a cost map for use in path planning. The library has been
+3D LiDAR scans. The library outputs a mesh of the world in real-time
+and a cost map for use in path planning. The library has been
 used for navigating Autonomous Mobile Robots (AMRs) and for `motion
 generation for robotic arms <https://ieeexplore.ieee.org/document/10160765>`_.
 The library is optimized for discrete NVIDIA GPUs and Jetson devices.
 
 Nvblox builds a reconstructed map in the form of a TSDF
-(Truncated Signed Distance Function) stored in a 3D voxel grid. This
-approach is similar to 3D occupancy grid mapping approaches in which
-occupancy probabilities are stored at each voxel. However, TSDF-based
+(Truncated Signed Distance Function) stored in a 3D voxel grid. TSDF-based
 approaches like nvblox store the (signed) distance to the closest
 surface at each voxel. The surface of the environment can then be
-extracted as the zero-level set of this voxelized function. Typically,
+extracted as the zero-level set of this voxelized function. This
+approach is similar to 3D occupancy grid mapping approaches in which
+occupancy probabilities are stored at each voxel. Typically,
 TSDF-based reconstructions provide higher quality surface
 reconstructions.
 
-In addition to their use in (surface) reconstruction, distance fields
-are also useful for path planning. In particular, they provide an
+Distance fields are useful for path planning. They provide an
 immediate means of checking potential future robot positions for
-collisions with the reconstructed environment. Therefore, in addition
-to the surface reconstruction (the TSDF), nvblox allows construction
-of the full (non-truncated) distance field, typically called the ESDF
-(Euclidean Signed Distance Function) in robotics literature.
+collisions with the reconstructed environment. nvblox provides for construction
+of the full (non-truncated) distance field, also known as the ESDF
+(Euclidean Signed Distance Function).
 
-The dual utility of distance functions for both reconstruction and
-planning motivates their use in nvblox (a reconstruction library for
-path planning).
+The dual utility of distance functions for reconstruction and
+planning motivates their use in nvblox.
 
-If you want to know more about how nvblox works behind
-the scenes refer to the :doc:`technical_details`.
+For more information on nvblox see :doc:`technical_details`.
 
 .. toctree::
    :hidden:
@@ -46,7 +42,7 @@ the scenes refer to the :doc:`technical_details`.
 Examples
 ---------
 
-To get started with nvblox, check out the following suggested examples:
+To get started with nvblox, review the following examples:
 
 .. toctree::
    :glob: