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docs/how-to-guides/.pages

@@ -1,7 +1,7 @@
 nav:
   - index.md
   - advanced-usage-of-colcon.md
-  - creating-maps-for-autoware.md
+  - creating-maps-for-autoware
   - determining-component-dependencies.md
   - integrating-autoware-with-your-vehicle.md
   - integrating-autoware-with-a-diff-drive-vehicle.md

docs/how-to-guides/creating-maps-for-autoware/.pages

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+nav:
+  - index.md
+  - Open-source SLAM algorithms: open-source-slam

docs/how-to-guides/creating-maps-for-autoware/index.md

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+# Creating maps for Autoware
+
+Autoware relies on high-definition point cloud maps and vector maps of the driving environment to perform various tasks such as localization, route planning, traffic light detection, and predicting the trajectories of pedestrians and other vehicles.
+
+The specifications for point cloud and vector maps required by Autoware are given below, along with examples of both open-source and proprietary software that you can use to create them.
+
+## Point cloud maps
+
+A 3D point cloud map is primarily used for LiDAR-based localization in Autoware. In order to determine the current position and orientation of the vehicle, a live scan captured from one or more LiDAR units is matched against a pre-generated 3D point cloud map. Therefore, an accurate point cloud map is crucial for good localization results.
+
+### Point cloud map specifications
+
+- It must cover the entire operational area of the vehicle and should include an additional buffer zone of at least 200 m in all directions.
+- It must be saved using the [PCD (Point Cloud Data) file format](https://pointclouds.org/documentation/tutorials/pcd_file_format.html), but can be a single PCD file or divided into multiple PCD files.
+- Each point in the map must contain X, Y, and Z coordinates.
+- An intensity or RGB value for each point may be optionally included.
+- Its file size must be smaller than 1 GB, [as per the current ROS message size limit](https://github.com/ros/ros_comm/issues/902).
+- Its resolution should be at least 0.2 m to yield reliable localization results.
+- It can be in either local or global coordinates, but must be in global coordinates (georeferenced) to use GNSS data for localization.
+
+!!! note
+
+    Three global coordinate systems are supported by Autoware, including [Military Grid Reference System (MGRS)](https://en.wikipedia.org/wiki/Military_Grid_Reference_System), [Universal Transverse Mercator (UTM)](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system), and [Japan Rectangular Coordinate System](https://ja.wikipedia.org/wiki/%E5%B9%B3%E9%9D%A2%E7%9B%B4%E8%A7%92%E5%BA%A7%E6%A8%99%E7%B3%BB).
+    However, MGRS is a preferred coordinate system for georeferenced maps.
+    In a map with MGRS coordinate system, the X and Y coordinates of each point represent the point's location within the 100,000-meter square, while the Z coordinate represents the point's elevation.
+
+### Creating a point cloud map
+
+Traditionally, a Mobile Mapping System (MMS) is used in order to create highly accurate large-scale point cloud maps. However, since a MMS requires high-end sensors for precise positioning, its operational cost can be very expensive and may not be suitable for a relatively small driving environment. Alternatively, a Simultaneous Localization And Mapping (SLAM) algorithm can be used to create a point cloud map from recorded LiDAR scans.
+
+Commonly used open-source SLAM implementations are [lidarslam-ros2](https://github.com/rsasaki0109/lidarslam_ros2) (LiDAR, IMU\*) and [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM/tree/ros2) (LiDAR, IMU, GNSS). The required sensor data for each algorithm is specified in the parentheses, where an asterisk (\*) indicates that such sensor data is optional. For supported LiDAR models, please check the Github repository of each algorithm. While these ROS 2-based SLAM implementations can be easily installed and used directly on the same machine that runs Autoware, it is important to note that they may not be as well-tested or as mature as ROS 1-based alternatives.
+
+The notable open-source SLAM implementations that are based on ROS 1 include [hdl-graph-slam](https://github.com/koide3/hdl_graph_slam) (LiDAR, IMU\*, GNSS\*), [LeGO-LOAM](https://github.com/facontidavide/LeGO-LOAM-BOR) (LiDAR, IMU\*), [LeGO-LOAM-BOR](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM) (LiDAR), and [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM) (LiDAR, IMU, GNSS).
+Most of these algorithms already have a built-in loop-closure and pose graph optimization. However, if the built-in, automatic loop-closure fails or does not work correctly, you can use [Interactive SLAM](https://github.com/SMRT-AIST/interactive_slam) to adjust and optimize a pose graph manually.
+
+Since Autoware is based on ROS 2, it could be problematic if you want to install ROS 1-based SLAM implementations on the same machine. To avoid this problem, you can use [Docker](https://www.docker.com/) or simply install them on a different machine. Another problem is the ROSBAG version; these SLAM implementations require ROSBAG 1 instead of ROSBAG 2 used by Autoware. For the ROSBAG version problem, you may use this [stand-alone converter](https://gitlab.com/MapIV/rosbags) to convert a ROSBAG 2 file to a ROSBAG 1 file and vice versa.
+
+If you prefer proprietary software that is easy to use, you can try a fully automatic mapping tool from [MAP IV, Inc.](https://www.map4.jp/), [_MapIV Engine_](https://www.map4.jp/map4_engine_en). They currently provide a trial license for Autoware users free of charge.
+
+## Vector maps
+
+A vector map contains highly accurate information about a road network, lane geometry, and traffic lights. It is required for route planning, traffic light detection, and predicting the trajectories of other vehicles and pedestrians.
+
+### Vector map specifications
+
+- It must cover the entire operational area of the vehicle and should include an additional buffer zone of at least 200 m in all directions.
+- It must be in [Lanelet2](https://github.com/fzi-forschungszentrum-informatik/Lanelet2) format, with [additional modifications required by Autoware](https://github.com/autowarefoundation/autoware_common/blob/main/tmp/lanelet2_extension/docs/lanelet2_format_extension.md).
+- It must contain the shape and position information of lanes, traffic lights, stop lines, crosswalks, parking spaces, and parking lots.
+- Each lanelet in the map must contain information regarding its right of way, speed limit, traffic direction, associated traffic lights, stop lines, and traffic signs.
+- Except at the beginning or end of a road, each lanelet in the map must be correctly connected to its predecessor, successors, left neighbor, and right neighbor.
+
+### Creating a vector map
+
+The easiest way to create an Autoware-compatible vector map is to use [Vector Map Builder](https://tools.tier4.jp/feature/vector_map_builder_ll2/), a free web-based tool provided by [TIER IV, Inc.](https://www.tier4.jp/).
+Vector Map Builder allows you to create lanes and add additional regulatory elements such as stop signs or traffic lights using a point cloud map as a reference.
+
+For open-source software options, [MapToolbox](https://github.com/autocore-ai/MapToolbox) is a plugin for [Unity](https://unity.com/) specifically designed to create Lanelet2 maps for Autoware.
+Although [JOSM](https://josm.openstreetmap.de/) is another open-source tool that can be used to create Lanelet2 maps, be aware that a number of modifications must be done manually to make the map compatible with Autoware. This process can be tedious and time-consuming, so the use of JOSM is not recommended.
+
+## Autoware-compatible map providers
+
+If it is not possible to create HD maps yourself, you can use a mapping service from the following Autoware-compatible map providers instead:
+
+- [MAP IV, Inc.](https://www.map4.jp/)
+- [AISAN TECHNOLOGY CO., LTD.](https://www.aisantec.co.jp/)
+- [TomTom](https://www.tomtom.com/)
+
+The table below shows each company's mapping technology and the types of HD maps they support.
+
+| **Company**                                               | **Mapping technology** | **Available maps**          |
+| --------------------------------------------------------- | ---------------------- | --------------------------- |
+| [MAP IV, Inc.](https://www.map4.jp/)                      | SLAM                   | Point cloud and vector maps |
+| [AISAN TECHNOLOGY CO., LTD.](https://www.aisantec.co.jp/) | MMS                    | Point cloud and vector maps |
+| [TomTom](https://www.tomtom.com/)                         | MMS                    | Vector map\*                |
+
+!!! note
+
+    Maps provided by TomTom use their proprietary AutoStream format, not Lanelet2.
+    The open-source [AutoStreamForAutoware tool](https://github.com/tomtom-international/AutoStreamForAutoware) can be used to convert an AutoStream map to a Lanelet2 map.
+    However, the converter is still in its early stages and has some [known limitations](https://github.com/tomtom-international/AutoStreamForAutoware/blob/main/docs/known-issues.md).

docs/how-to-guides/creating-maps-for-autoware/open-source-slam/.pages

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+nav:
+  - index.md
+  - FAST-LIO-LC: fast-lio-lc
+  - FAST_LIO_SLAM: fast-lio-slam
+  - FD-SLAM: fd-slam
+  - hdl_graph_slam: hdl-graph-slam
+  - IA_LIO_SAM: ia-lio-slam
+  - iscloam: iscloam
+  - LeGO-LOAM-BOR: lego-loam-bor
+  - LIO-SAM: lio-sam
+  - Optimized-SC-F-LOAM: optimized-sc-f-loam
+  - SC-A-LOAM: sc-a-loam
+  - SC-LeGO-LOAM: sc-lego-loam

docs/how-to-guides/creating-maps-for-autoware/open-source-slam/fast-lio-lc/.pages

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+nav:
+  - index.md

docs/how-to-guides/creating-maps-for-autoware/open-source-slam/fast-lio-lc/index.md

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+# FAST_LIO_LC
+
+## What is FAST_LIO_LC?
+
+- A computationally efficient and robust LiDAR-inertial odometry package with loop closure module and graph optimization.
+
+## Repository Information
+
+### Original Repository link
+
+[https://github.com/yanliang-wang/FAST_LIO_LC](https://github.com/yanliang-wang/FAST_LIO_LC)
+
+### Required Sensors
+
+- LIDAR [Velodyne, Ouster, Livox]
+- IMU [6-AXIS, 9-AXIS]
+- GPS [Optional]
+
+### ROS Compatibility
+
+- ROS 1
+
+### Dependencies
+
+- Ubunutu 18.04
+- ROS Melodic
+- PCL >= 1.8, Follow [PCL Installation](https://pointclouds.org/downloads/#linux).
+- Eigen >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.php?title=Main_Page).
+- GTSAM >= 4.0.0, Follow [GTSAM Installation](https://gtsam.org/get_started).
+
+```bash
+  wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip
+  cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/
+  cd ~/Downloads/gtsam-4.0.0-alpha2/
+  mkdir build && cd build
+  cmake ..
+  sudo make install
+```
+
+## Build & Run
+
+### 1) Build
+
+```bash
+    mkdir -p ~/ws_fastlio_lc/src
+    cd ~/ws_fastlio_lc/src
+    git clone https://github.com/gisbi-kim/FAST_LIO_SLAM.git
+    git clone https://github.com/Livox-SDK/livox_ros_driver
+    cd ..
+    catkin_make
+```
+
+### 2) Set parameters
+
+- 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" >
+
+- For imu-lidar compatibility, extrinsic matrices from calibration must be changed.
+
+<p> <img src="images/extrinsic.png" alt="Extrinsic Matrices"></p>
+
+- To enable auto-save, `pcd_save_enable` must be `1` from the launch file (`workspace/src/FAST_LIO_LC/FAST_LIO/launch/mapping_ouster64_mulran.launch`).
+
+### 3) Run
+
+- For Ouster OS1-64
+
+      # open new terminal: run FAST-LIO
+      roslaunch fast_lio mapping_ouster64.launch
+
+      # open the other terminal tab: run SC-PGO
+      roslaunch aloam_velodyne fastlio_ouster64.launch
+
+      # play bag file in the other terminal
+      rosbag play RECORDED_BAG.bag --clock
+
+<p> <img src="images/launch.png" width="712" alt="launch"></p>
+
+## 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>
+
+## Other Examples
+
+<p> <img src="images/fast-lio-lc-output.png" width="712" alt="example_results"></p>
+
+## Example dataset
+
+Check original repository link for example dataset.
+
+## Contact
+
+- Maintainer: Yanliang Wang (`wyl410922@qq.com`)
+
+## Acknowledgements
+
+- Thanks for [FAST_LIO](https://github.com/hku-mars/FAST_LIO) authors.
+
+<!-- In this project, the LIO module refers to FAST-LIO and the pose graph optimization refers to FAST_LIO_SLAM.
+
+Many thanks for their work. -->

docs/how-to-guides/creating-maps-for-autoware/open-source-slam/fast-lio-slam/.pages

@@ -0,0 +1,2 @@
+nav:
+  - index.md

docs/how-to-guides/creating-maps-for-autoware/open-source-slam/fast-lio-slam/index.md

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+# FAST_LIO_SLAM
+
+## What is FAST_LIO_SLAM?
+
+- FAST_LIO_SLAM is the integration of FAST_LIO and SC-PGO which is scan context based loop detection and GTSAM based pose-graph optimization.
+
+## Repository Information
+
+### Original Repository link
+
+[https://github.com/gisbi-kim/FAST_LIO_SLAM](https://github.com/gisbi-kim/FAST_LIO_SLAM)
+
+### Required Sensors
+
+- LIDAR [Livox, Velodyne, Ouster]
+- IMU [6-AXIS, 9-AXIS]
+- GPS [OPTIONAL]
+
+### ROS Compatibility
+
+- ROS 1
+
+### Dependencies
+
+- ROS
+- PCL
+- Gtsam
+
+```bash
+wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip
+cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/
+cd ~/Downloads/gtsam-4.0.0-alpha2/
+mkdir build && cd build
+cmake ..
+sudo make install
+```
+
+- PCL >= 1.8, Follow [PCL Installation](https://pointclouds.org/downloads/#linux).
+- Eigen >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.php?title=Main_Page).
+
+## Build & Run
+
+### 1) Build
+
+```bash
+    mkdir -p ~/catkin_fastlio_slam/src
+    cd ~/catkin_fastlio_slam/src
+    git clone https://github.com/gisbi-kim/FAST_LIO_SLAM.git
+    git clone https://github.com/Livox-SDK/livox_ros_driver
+    cd ..
+    catkin_make
+    source devel/setup.bash
+```
+
+### 2) Set parameters
+
+- Set imu and lidar topic on `Fast_LIO/config/ouster64.yaml`
+
+### 3) Run
+
+```bash
+    # terminal 1: run FAST-LIO2
+    roslaunch fast_lio mapping_ouster64.launch
+
+    # open the other terminal tab: run SC-PGO
+    cd ~/catkin_fastlio_slam
+    source devel/setup.bash
+    roslaunch aloam_velodyne fastlio_ouster64.launch
+
+    # play bag file in the other terminal
+    rosbag play xxx.bag -- clock --pause
+```
+
+## Example Result
+
+<p><img src="images/fast_lio_slam.png" width=712pix></p>
+
+<p><img src="images/fast_lio_slam.png" width=712pix></p>
+
+## Other Examples
+
+- [Tutorial video 1](https://youtu.be/nu8j4yaBMnw) (using KAIST 03 sequence of [MulRan dataset](https://sites.google.com/view/mulran-pr/dataset))
+
+  - 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
+
+- [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
+
+## Acknowledgements
+
+- 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`)

docs/how-to-guides/creating-maps-for-autoware/open-source-slam/fd-slam/.pages

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+nav:
+  - index.md

docs/how-to-guides/creating-maps-for-autoware/open-source-slam/fd-slam/index.md

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+# FD-SLAM
+
+## What is FD-SLAM?
+
+- 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
+
+This is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR.
+
+It is based on hdl_graph_slam and the steps to run our system are same with hdl-graph-slam.
+
+### Original Repository link
+
+[https://github.com/SLAMWang/FD-SLAM](https://github.com/SLAMWang/FD-SLAM)
+
+### Required Sensors
+
+- LIDAR[VLP-16, HDL-32, HDL-64, OS1-64]
+- GPS
+- IMU [Optional]
+
+### ROS Compatibility
+
+- ROS 1
+
+### Dependencies
+
+- [ROS](http://wiki.ros.org/noetic/Installation/Ubuntu)
+- [PCL](https://pointclouds.org/downloads/#linux)
+- [g2o](http://wiki.ros.org/g2o)
+- [Suitesparse](https://github.com/ethz-asl/suitesparse)
+
+The following ROS packages are required:
+
+- geodesy
+- nmea_msgs
+- pcl_ros
+- [ndt_omp](https://github.com/koide3/ndt_omp)
+- [U_gicp](https://github.com/SLAMWang/UGICP) This is modified based on [fast_gicp](https://github.com/SMRT-AIST/fast_gicp) by us. We use UGICP for keyframe matching.
+
+## Build & Run
+
+### 1) Build
+
+```bash
+cd ~/catkin_ws/src
+git clone https://github.com/SLAMWang/FD-SLAM.git
+cd ..
+catkin_make
+```
+
+### 2) Services
+
+```bash
+/hdl_graph_slam/dump  (hdl_graph_slam/DumpGraph)
+  - save all the internal data (point clouds, floor coeffs, odoms, and pose graph) to a directory.
+
+/hdl_graph_slam/save_map (hdl_graph_slam/SaveMap)
+  - save the generated map as a PCD file.
+```
+
+### 3) Set parameters
+
+- All the configurable parameters are listed in _launch/\*\*\*\*.launch_ as ros params.
+
+### 4) Run
+
+```bash
+source devel/setup.bash
+roslaunch hdl_graph_slam hdl_graph_slam_400_ours.launch
+```

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