fix: Changed the explanation to focus on XT32 LiDAR related to discontinue…

docs/tutorials/01_hardware_setup.md

@@ -4,6 +4,10 @@
 
 ## Sample hardware configuration
 
+> [!WARNING]
+>
+> HESAI AT128 is deprecated because it has been discontinued.
+
 This following hardware configuration is used throughout this tutorial.
 
 - ECU setup
@@ -12,10 +16,10 @@
 - Sensor setup
   - Sample configuration 1
     - Camera: TIER IV Automotive HDR Camera C1 (x2)
-    - LiDAR: HESAI AT128 (x1)
+    - LiDAR: HESAI Pandar XT32 (x1)
   - Sample configuration 2
     - Camera: TIER IV Automotive HDR Camera C1 (x2)
-    - LiDAR: HESAI Pandar XT32 (x1)
+    - LiDAR: HESAI AT128 (x1)
 
 ### Connection diagram
 
@@ -39,16 +43,16 @@
 
 ### Sensor driver
 
 Edge.Auto supports a variety of sensor types. The following repositories are used to make those sensors available in your ROS2 environment.
 Please refer to the each repositories for more details.
 
 - Camera driver
   - [tier4/tier4_automotive_hdr_camera](https://github.com/tier4/tier4_automotive_hdr_camera): Kernel driver for using TIER IV cameras with Video4Linux2 interface.
   - [tier4/ros2_v4l2_camera](https://github.com/tier4/ros2_v4l2_camera): ROS2 package for camera driver using Video4Linux2.
 - LIDAR driver
   - [tier4/nebula](https://github.com/tier4/nebula): ROS2 package for unified ethernet-based LiDAR driver.
 - Sensor synchronization
   - [tier4/sensor_trigger](https://github.com/tier4/sensor_trigger): ROS2 package for generating sensor trigger signals.
 
 ### Sensor/ECU synchronization
 

docs/tutorials/03_sensor_calibration.md

@@ -96,9 +96,9 @@ for detailed operation on the tool.
 cd edge-auto
 source install/setup.bash
 
-ros2 launch edge_auto_launch calibration_extrinsic_at128_sample.launch.xml
-## or
 ros2 launch edge_auto_launch calibration_extrinsic_xt32_sample.launch.xml
+## or
+ros2 launch edge_auto_launch calibration_extrinsic_at128_sample.launch.xml
 ```
 
 After executing the above launch file, two windows will be appear like below:
@@ -115,6 +115,6 @@ After calculating extrinsic parameters, put the result in the appropriate files
 ```sh
 edge-auto/src/individual_params/individual_params/config/
 └── default
-    ├── at128_to_camera0.json # <- replace this file with your calculated results
-    └── at128_to_camera1.json # <- replace this file with your calculated results
+    ├── xt32_to_camera0.json # <- replace this file with your calculated results
+    └── xt32_to_camera1.json # <- replace this file with your calculated results
 ```

docs/tutorials/04_launch_application.md

@@ -33,16 +33,16 @@
 ![demo_construction](figures/demo_construction.drawio.svg "demo_construction.svg")
 
 The launch files can take an argument named `sensor_height` that represents LiDAR height from the ground in meters (default: 1.0).
 Because some functions in Auoware are designed to perform best using previously known LiDAR height,
 it is recommended to adjust this value according to your actual sensor setup to acquire better performance.
 
 ```sh
 cd edge-auto
 source install/setup.bash
 
-ros2 launch edge_auto_launch perception_at128_sample.launch.xml sensor_height:=[sensor height from the ground]
-## or
 ros2 launch edge_auto_launch perception_xt32_sample.launch.xml sensor_height:=[sensor height from the ground]
+## or
+ros2 launch edge_auto_launch perception_at128_sample.launch.xml sensor_height:=[sensor height from the ground]
 ```
 
 This sample mainly leverages [pointcloud_preprocessor](https://github.com/autowarefoundation/autoware.universe/tree/main/sensing/pointcloud_preprocessor), [centerpoint](https://github.com/autowarefoundation/autoware.universe/tree/main/perception/lidar_centerpoint), and [image_projection_based_fusion](https://github.com/autowarefoundation/autoware.universe/tree/main/perception/image_projection_based_fusion) packages
@@ -51,14 +51,14 @@
 
 In addition to the perception stack, this sample also launches viewers so that users can check perception results visually.
 
-As an example, the following picture shows the perception results in the case of a system configuration that consists of one AT128 and one C1 camera.
+As an example, the following picture shows the perception results.
 
 ![Example: perception result](../sample.png "Example: perception result")
 
 Note: The default models used in this tutorial are tuned for outdoor environments
 (especially for autonomous driving contexts).
 If you try this tutorial in some indoor environments, such as room ceil is in the range of sensor FoV, 
 additional preprocessings, such as cropping the range to be processed, may be required to get better results.
 
 ## (Experimental) Launch experimental repositories