diff --git a/docs/how-to-guides/integrating-autoware/creating-maps/open-source-slam/index.md b/docs/how-to-guides/integrating-autoware/creating-maps/open-source-slam/index.md
index c311752bd2..a345f3fbf6 100644
--- a/docs/how-to-guides/integrating-autoware/creating-maps/open-source-slam/index.md
+++ b/docs/how-to-guides/integrating-autoware/creating-maps/open-source-slam/index.md
@@ -27,14 +27,15 @@ Most of these algorithms already have a built-in loop-closure and pose graph opt
| FAST-LIO-LC | A computationally efficient and robust LiDAR-inertial odometry package with loop closure module and graph optimization | [github.com/yanliang-wang/FAST_LIO_LC](https://github.com/yanliang-wang/FAST_LIO_LC) | ✔️ | Lidar
IMU
GPS [Optional] | ROS 1 | ROS Melodic
PCL >= 1.8
Eigen >= 3.3.4
GTSAM >= 4.0.0 |
| 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 | [github.com/gisbi-kim/FAST_LIO_SLAM](https://github.com/gisbi-kim/FAST_LIO_SLAM) | ✔️ | Lidar
IMU
GPS [Optional] | ROS 1 | PCL >= 1.8
Eigen >= 3.3.4 |
| 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 | [github.com/SLAMWang/FD-SLAM](https://github.com/SLAMWang/FD-SLAM) | ✔️ | Lidar
IMU [Optional]
GPS | ROS 1 | PCL
g2o
Suitesparse |
-| GenZ-ICP | GenZ-ICP is a Generalizable and Degeneracy-Robust LiDAR Odometry Using an Adaptive Weighting | [github.com/cocel-postech/genz-icp](https://github.com/cocel-postech/genz-icp) | ❌ | Lidar | **ROS 2** | No extra dependency |
+| GenZ-ICP | GenZ-ICP is a Generalizable and Degeneracy-Robust LiDAR Odometry Using an Adaptive Weighting | [github.com/cocel-postech/genz-icp](https://github.com/cocel-postech/genz-icp) | ❌ | Lidar | **ROS 2** | No extra dependency |
| hdl_graph_slam | An open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. It also supports several graph constraints, such as GPS, IMU acceleration (gravity vector), IMU orientation (magnetic sensor), and floor plane (detected in a point cloud) | [github.com/koide3/hdl_graph_slam](https://github.com/koide3/hdl_graph_slam) | ✔️ | Lidar
IMU [Optional]
GPS [Optional] | ROS 1 | PCL
g2o
OpenMP |
| IA-LIO-SAM | IA_LIO_SLAM is created for data acquisition in unstructured environment and it is a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping that achieves highly accurate robot trajectories and mapping | [github.com/minwoo0611/IA_LIO_SAM](https://github.com/minwoo0611/IA_LIO_SAM) | ✔️ | Lidar
IMU
GPS | ROS 1 | GTSAM |
| ISCLOAM | ISCLOAM presents a robust loop closure detection approach by integrating both geometry and intensity information | [github.com/wh200720041/iscloam](https://github.com/wh200720041/iscloam) | ✔️ | Lidar | ROS 1 | Ubuntu 18.04
ROS Melodic
Ceres
PCL
GTSAM
OpenCV |
| KISS-ICP | A simple and fast ICP algorithm for 3D point cloud registration | [github.com/PRBonn/kiss-icp](https://github.com/PRBonn/kiss-icp) | ❌ | Lidar | **ROS 2** | No extra dependency |
+| Kinematic-ICP | Kinematic-ICP is a LiDAR odometry approach that explicitly incorporates the kinematic constraints of mobile robots into the classic point-to-point ICP algorithm. | [github.com/PRBonn/kinematic-icp](https://github.com/PRBonn/kinematic-icp) | ❌ | Lidar
Odometry | **ROS 2** | No extra dependency |
| LeGO-LOAM-BOR | LeGO-LOAM-BOR is improved version of the LeGO-LOAM by improving quality of the code, making it more readable and consistent. Also, performance is improved by converting processes to multi-threaded approach | [github.com/facontidavide/LeGO-LOAM-BOR](https://github.com/facontidavide/LeGO-LOAM-BOR) ROS2 fork: [/github.com/eperdices/LeGO-LOAM-SR](https://github.com/eperdices/LeGO-LOAM-SR) | ✔️ | Lidar
IMU | ROS 1
**ROS 2** | ROS 1/2
PCL
GTSAM |
| LIO_SAM | A framework that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. It formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system | [github.com/TixiaoShan/LIO-SAM](https://github.com/TixiaoShan/LIO-SAM) | ✔️ | Lidar
IMU
GPS [Optional] | ROS 1
**ROS 2** | PCL
GTSAM |
-| li_slam_ros2 | li_slam package is a combination of lidarslam_ros2 and the LIO-SAM IMU composite method. | [github.com/rsasaki0109/li_slam_ros2](https://github.com/rsasaki0109/li_slam_ros2) | ✔️ | Lidar
IMU
GPS [Optional] | **ROS 2** | PCL
GTSAM |
+| li_slam_ros2 | li_slam package is a combination of lidarslam_ros2 and the LIO-SAM IMU composite method. | [github.com/rsasaki0109/li_slam_ros2](https://github.com/rsasaki0109/li_slam_ros2) | ✔️ | Lidar
IMU
GPS [Optional] | **ROS 2** | PCL
GTSAM |
| Optimized-SC-F-LOAM | An improved version of F-LOAM and uses an adaptive threshold to further judge the loop closure detection results and reducing false loop closure detections. Also it uses feature point-based matching to calculate the constraints between a pair of loop closure frame point clouds and decreases time consumption of constructing loop frame constraints | [github.com/SlamCabbage/Optimized-SC-F-LOAM](https://github.com/SlamCabbage/Optimized-SC-F-LOAM) | ✔️ | Lidar | ROS 1 | PCL
GTSAM
Ceres |
| SC-A-LOAM | A real-time LiDAR SLAM package that integrates A-LOAM and ScanContext. | [github.com/gisbi-kim/SC-A-LOAM](https://github.com/gisbi-kim/SC-A-LOAM) | ✔️ | Lidar | ROS 1 | GTSAM >= 4.0 |
| SC-LeGO-LOAM | SC-LeGO-LOAM integrated LeGO-LOAM for lidar odometry and 2 different loop closure methods: ScanContext and Radius search based loop closure. While ScanContext is correcting large drifts, radius search based method is good for fine-stitching | [github.com/gisbi-kim/SC-LeGO-LOAM](https://github.com/gisbi-kim/SC-LeGO-LOAM) | ✔️ | Lidar
IMU | ROS 1 | PCL
GTSAM |