docs: fix cpell check failed (#421)

* fix ROS1, ROS2,Github Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * style(pre-commit): autofix * fix typo Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * fix image path and image name Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * style(pre-commit): autofix * fix gtsam ->GTSAM Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * fix typo Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * add inline ignore words Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * style(pre-commit): autofix * fix dead link Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * update by precommit Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * fix ROS2 and organization names Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * feat: use `pose_source` and `twist_source` for selecting localization methods (#418) * feat: change localization methods section Signed-off-by: kminoda <koji.minoda@tier4.jp> * style(pre-commit): autofix * fix typo Signed-off-by: kminoda <koji.minoda@tier4.jp> * minor fix Signed-off-by: kminoda <koji.minoda@tier4.jp> * Update docs/how-to-guides/integrating-autoware/launch-autoware/localization-methods/index.md Co-authored-by: Kento Yabuuchi <moc.liamg.8y8@gmail.com> --------- Signed-off-by: kminoda <koji.minoda@tier4.jp> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kento Yabuuchi <moc.liamg.8y8@gmail.com> * style(pre-commit): autofix * fix Azim and Elev Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * fix product and interface names and ignore words Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> * style(pre-commit): autofix --------- Signed-off-by: asa-naki <akihisa.nagata@tier4.jp> Signed-off-by: kminoda <koji.minoda@tier4.jp> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: kminoda <44218668+kminoda@users.noreply.github.com> Co-authored-by: Kento Yabuuchi <moc.liamg.8y8@gmail.com>
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diff --git a/...-competitions/images/aichallenge_2023.png → ...competitions/images/ai_challenge_2023.png b/...-competitions/images/aichallenge_2023.png → ...competitions/images/ai_challenge_2023.png
diff --git a/docs/autoware-competitions/index.md b/docs/autoware-competitions/index.md
@@ -4,8 +4,8 @@ This page is a collection of the links to the competitions that are related to t
 
 |                             Title                             | Status  | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
 | :-----------------------------------------------------------: | :-----: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| <img src="images/autoware_challenge_2023.png" width = 640px > | Ongoing | **[Autoware / TIER IV Challenge 2023](https://www.autoware.org/autoware-challenge-2023)** <br> **Date:** May 15, 2023 - Nov. 1st, 2023 <br><br> As one of the main contributors of Autoware, TIER IV has been facing many difficult challenges through development, and TIER IV would like to sponsor a challenge to solve such engineering challenges. Any researchers, students, individuals or organizations are welcome to participate and submit their solution to any of the challenges we propose.                                                                                                                                                                                   |
-|    <img src="images/aichallenge_2023.png" width = 640px >     | Ongoing | **[Japan Automotive AI Challenge 2023](https://www.jsae.or.jp/jaaic/)** <br> **Registration:** June 5, 2023 - July 14, 2023 <br>**Qualifiers:** July 3, 2023 - Aug. 31, 2023 <br>**Finals:** Nov. 12, 2023<br><br> In this competition, we focus on challenging tasks posed by autonomous driving in factory environments and aim to develop Autoware-based AD software that can overcome them. The qualifiers use the [digital twin autonomous driving simulator AWSIM](https://tier4.github.io/AWSIM/) to complete specific tasks within a virtual environment. Teams that make it to the finals have the opportunity to run their software on actual vehicles in a test course in Japan. |
+| <img src="images/autoware_challenge_2023.png" width = 640px > | Ongoing | **[Autoware / TIER IV Challenge 2023](https://autoware.org/autoware-challenge-2023)** <br> **Date:** May 15, 2023 - Nov. 1st, 2023 <br><br> As one of the main contributors of Autoware, TIER IV has been facing many difficult challenges through development, and TIER IV would like to sponsor a challenge to solve such engineering challenges. Any researchers, students, individuals or organizations are welcome to participate and submit their solution to any of the challenges we propose.                                                                                                                                                                                       |
+|    <img src="images/ai_challenge_2023.png" width = 640px >    | Ongoing | **[Japan Automotive AI Challenge 2023](https://www.jsae.or.jp/jaaic/)** <br> **Registration:** June 5, 2023 - July 14, 2023 <br>**Qualifiers:** July 3, 2023 - Aug. 31, 2023 <br>**Finals:** Nov. 12, 2023<br><br> In this competition, we focus on challenging tasks posed by autonomous driving in factory environments and aim to develop Autoware-based AD software that can overcome them. The qualifiers use the [digital twin autonomous driving simulator AWSIM](https://tier4.github.io/AWSIM/) to complete specific tasks within a virtual environment. Teams that make it to the finals have the opportunity to run their software on actual vehicles in a test course in Japan. |
 
 ## Proposing New Competition
 

diff --git a/docs/contributing/coding-guidelines/ros-nodes/launch-files.md b/docs/contributing/coding-guidelines/ros-nodes/launch-files.md
@@ -2,7 +2,7 @@
 
 ## Overview
 
-Autoware use ROS2 launch system to startup the software. Please see the [official documentation](https://docs.ros.org/en/humble/Tutorials/Intermediate/Launch/Launch-Main.html) to get a basic understanding about ROS 2 Launch system if you are not familiar with it.
+Autoware use ROS 2 launch system to startup the software. Please see the [official documentation](https://docs.ros.org/en/humble/Tutorials/Intermediate/Launch/Launch-Main.html) to get a basic understanding about ROS 2 Launch system if you are not familiar with it.
 
 ## Guideline
 
@@ -60,7 +60,7 @@ In order to automatically load the newly added package when starting Autoware, y
 
 ## Parameter management
 
-Another purpose of introducing the `autoware_launch` repository is to facilitate the parameter management of Autoware. Thinking about this situation: if we want to integrate Autoware to a specific vehicle and modify parameters, we have to fork `autoware.universe` which also has a lot of code other than parameters and is frequently updated by developers. By intergrating these parameters in `autoware_launch`, we can customize the Autoware parameters just by forking `autoware_launch` repository. Taking the localization module as an examples:
+Another purpose of introducing the `autoware_launch` repository is to facilitate the parameter management of Autoware. Thinking about this situation: if we want to integrate Autoware to a specific vehicle and modify parameters, we have to fork `autoware.universe` which also has a lot of code other than parameters and is frequently updated by developers. By integrating these parameters in `autoware_launch`, we can customize the Autoware parameters just by forking `autoware_launch` repository. Taking the localization module as an examples:
 
 1. all the “launch parameters” for localization component is listed in the files under `autoware_launch/autoware_launch/config/localization`.
 2. the "launch parameters" file paths are set in the `autoware_launch/autoware_launch/launch/components/tier4_localization_component.launch.xml` file.

diff --git a/docs/design/autoware-architecture/control/index.md b/docs/design/autoware-architecture/control/index.md
@@ -43,7 +43,7 @@ Note that vehicle-specific values such as pedal positions and low-level informat
 
 Autoware is designed to be an autonomous driving platform able to accommodate vehicles with various drivetrain types.
 
-This is an explanation of how Autoware handles the standardization of systems with different vehicle drivetrains. The interfaces for vehicle drivetrains are diverse, including steering angle, steering angular velocity, steering torque, speed, accel/brake pedals, and brake pressure. To accommodate these differences, Autoware adds an adapter module between the control component and the vehicle interface. This module performs the conversion between the proprietary message types used by the vehicle (such as brake pressure) and the generic types used by Autoware (such as desired acceleration). By providing this conversion information, the differences in vehicle drivetrain can be accommodated.
+This is an explanation of how Autoware handles the standardization of systems with different vehicle drivetrain. The interfaces for vehicle drivetrain are diverse, including steering angle, steering angular velocity, steering torque, speed, accel/brake pedals, and brake pressure. To accommodate these differences, Autoware adds an adapter module between the control component and the vehicle interface. This module performs the conversion between the proprietary message types used by the vehicle (such as brake pressure) and the generic types used by Autoware (such as desired acceleration). By providing this conversion information, the differences in vehicle drivetrain can be accommodated.
 
 If the information is not known in advance, an automatic calibration tool can be used. Calibration will occur within limited degrees of freedom, generating the information necessary for the drivetrain conversion automatically.
 
@@ -61,7 +61,7 @@ This is an example of the several drivetrain types in the vehicle interface.
 | JPN TAXI     | Steering angle       | Accel/brake pedal position                                        | Acceleration lookup table conversion for longitudinal                                                                                              |
 | GSM8         | Steering EPS voltage | Acceleration motor voltage, Deceleration brake hydraulic pressure | lookup table and PID conversion for lateral and longitudinal                                                                                       |
 | YMC Golfcart | Steering angle       | Velocity                                                          |                                                                                                                                                    |
-| Logiees      | yaw rate             | Velocity                                                          |                                                                                                                                                    |
+| Logiee       | yaw rate             | Velocity                                                          |                                                                                                                                                    |
 | F1 TENTH     | Steering angle       | Motor RPM                                                         | [interface code](https://gitlab.com/autowarefoundation/autoware.auto/AutowareAuto/-/blob/master/src/drivers/vesc_interface/src/vesc_interface.cpp) |
 
 ## Control Feature Design

diff --git a/...ast-lio-lc/images/fastlio_lc_example1.png → ...st-lio-lc/images/fast-lio-lc-example1.png b/...ast-lio-lc/images/fastlio_lc_example1.png → ...st-lio-lc/images/fast-lio-lc-example1.png
diff --git a/...ast-lio-lc/images/fastlio_lc_example2.png → ...st-lio-lc/images/fast-lio-lc-example2.png b/...ast-lio-lc/images/fastlio_lc_example2.png → ...st-lio-lc/images/fast-lio-lc-example2.png
diff --git a/...guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/index.md b/...guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/index.md
@@ -52,6 +52,8 @@
 
 ### 2) Set parameters
 
+<!-- cspell: ignore mulran -->
+
 - After downloading the repository, change topic and sensor settings on the config file (`workspace/src/FAST_LIO_LC/FAST_LIO/config/ouster64_mulran.yaml`) with the lidar topic name in your bag file.
 
  <img src="images/config_info.png" width="712" >
@@ -64,6 +66,8 @@
 
 ### 3) Run
 
+<!-- cspell: ignore aloam fastlio -->
+
 - For Ouster OS1-64
 
       # open new terminal: run FAST-LIO
@@ -79,8 +83,8 @@
 
 ## Example Result
 
-<p> <img src="images/fastlio_lc_example1.png" width="712" alt="example_results1"></p>
-<p> <img src="images/fastlio_lc_example2.png" width="712" alt="example_results2"></p>
+<p> <img src="images/fast-lio-lc-example1.png" width="712" alt="example_results1"></p>
+<p> <img src="images/fast-lio-lc-example2.png" width="712" alt="example_results2"></p>
 
 ## Other Examples
 
@@ -92,6 +96,8 @@ Check original repository link for example dataset.
 
 ## Contact
 
+<!-- cspell: ignore  Yanliang -->
+
 - Maintainer: Yanliang Wang (`wyl410922@qq.com`)
 
 ## Acknowledgements

diff --git a/...ides/integrating-autoware/creating-maps/open-source-slam/fast-lio-slam/index.md b/...ides/integrating-autoware/creating-maps/open-source-slam/fast-lio-slam/index.md
@@ -24,7 +24,7 @@
 
 - ROS
 - PCL
-- Gtsam
+- GTSAM
 
 ```bash
 wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip
@@ -83,15 +83,17 @@ sudo make install
 
   - Example result captures
     <p><img src="images/kaist03.png" width=712pix></p>
-  - [download the KAIST03 pcd map](https://www.dropbox.com/s/w599ozdg7h6215q/KAIST03.pcd?dl=0) made by FAST-LIO-SLAM, 500MB
+  - [download the KAIST 03 pcd map](https://www.dropbox.com/s/w599ozdg7h6215q/KAIST03.pcd?dl=0) made by FAST-LIO-SLAM, 500MB
 
 - [Example Video 2](https://youtu.be/94mC05PesvQ) (Riverside 02 sequence of [MulRan dataset](https://sites.google.com/view/mulran-pr/dataset))
   - Example result captures
     <p><img src="images/riverside02.png" width=712pix></p>
-  - [download the Riverisde02 pcd map](https://www.dropbox.com/s/1aolth7ry4odxo4/Riverside02.pcd?dl=0) made by FAST-LIO-SLAM, 400MB
+  - [download the Riverside 02 pcd map](https://www.dropbox.com/s/1aolth7ry4odxo4/Riverside02.pcd?dl=0) made by FAST-LIO-SLAM, 400MB
 
 ## Acknowledgements
 
+<!-- cspell: ignore Giseop -->
+
 - Thanks for [FAST_LIO](https://github.com/hku-mars/FAST_LIO) authors.
 - You may have an interest in [this version of FAST-LIO + Loop closure](https://github.com/yanliang-wang/FAST_LIO_LC), implemented by [yanliang-wang](https://github.com/yanliang-wang)
 - Maintainer: Giseop Kim (`paulgkim@kaist.ac.kr`)
diff --git a/...-to-guides/integrating-autoware/creating-maps/open-source-slam/fd-slam/index.md b/...-to-guides/integrating-autoware/creating-maps/open-source-slam/fd-slam/index.md
@@ -2,6 +2,8 @@
 
 ## What is FD-SLAM?
 
+<!-- cspell: ignore UGICP Suitesparse -->
+
 - FD_SLAM is Feature&Distribution-based 3D LiDAR SLAM method based on Surface Representation Refinement. In this algorithm novel feature-based Lidar odometry used for fast scan-matching, and used a proposed UGICP method for keyframe matching.
 
 ## Repository Information

diff --git a/...guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/index.md b/...guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/index.md
@@ -41,7 +41,7 @@
     ```
 
 - [GTSAM](https://github.com/borglab/gtsam/releases) (Georgia Tech Smoothing and Mapping library)
-
+  <!-- cspell: ignore DGTSAM -->
   ```bash
   wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.2.zip
   cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/
@@ -96,7 +96,9 @@ Check original repo link for example dataset.
 
 ## Contact
 
-- Maintainer: Kevin Jung (`Github: minwoo0611`)
+<!-- cspell: ignore minwoo -->
+
+- Maintainer: Kevin Jung (`GitHub: minwoo0611`)
 
 ## Paper
 
@@ -117,4 +119,6 @@ Part of the code is adapted from [LIO-SAM (IROS-2020)](https://github.com/Tixiao
 
 ## Acknowledgements
 
+<!-- cspell: ignore Englot  Ratti -->
+
 - IA-LIO-SAM is based on LIO-SAM (T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus. LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping).