fix(markdown): change to appropriate headings

Author: JoshuaTsai0520

wiki/state-estimation/lio-sam-velocity-undistortion-and-dynamic-filtering.md

@@ -19,7 +19,7 @@ The detailed explanation of the LIO-SAM algorithm and its configuration settings
 
 After the point cloud map is built, we will also describe a simple approach for removing dynamic obstacles and outliers from the map. Our goal is to generate a static, clean, and high-quality 3D point cloud map.
 
-# Dependencies
+## Dependencies
 Our improved version is tested with ROS2 Humble on Ubuntu 22.04.
 
 Refer to the [Dependencies](https://github.com/TixiaoShan/LIO-SAM/tree/ros2) in LIO-SAM's github ros2 branch.
@@ -44,7 +44,7 @@ Use the following script to install [Sophus](https://github.com/strasdat/Sophus.
 bash install_sophus.sh
 ```
 
-# Installation
+## Installation
 Use the following commands to compile the package:
 
 ```
@@ -54,7 +54,7 @@ git checkout ros2-velocity
 bash build_lio.sh
 ```
 
-# Run the package
+## Run the package
 The source command is already written in the script:
 ```
 bash run_lio.sh
@@ -65,28 +65,28 @@ Then play bag files:
 ros2 bag play your-bag
 ```
 
-# Core Configuration Setup
+## Core Configuration Setup
 
-## Input Topic
+### Input Topic
 - pointCloudTopic (sensor_msgs/msg/PointCloud2)
 - imuTopic (sensor_msgs/msg/Imu)
 - twistTopic (geometry_msgs/msg/TwistStamped) (Optional)
 - gpsTopic (nav_msgs::msg::Odometry) (Optional)
 
-## Distortion Function
+### Distortion Function
 - enable_distortion_function: Open if you want to use distortion correction algorithm
 - use_imu: Use IMU data in distortion function
 - use_velocity: Use velocity data in distortion function
 
-## IMU Settings
+### IMU Settings
 - extrinsicTrans: Translation between LiDAR and IMU
 - extrinsicRot, extrinsicRPY: Rotation between LiDAR and IMU
 
-## Loop Closure
+### Loop Closure
 - loopClosureEnableFlag: Open loop closing
 - historyKeyframeFitnessScore: ICP matching threshold, adjust it if loop closing performance is bad
 
-# Distortion Correction Module
+## Distortion Correction Module
 LiDAR motion distortion occurs when the sensor or robot moves while a frame is being captured. Since a rotating LiDAR does not measure all points at the same instant, different parts of the point cloud correspond to slightly different sensor poses. This can cause straight walls to appear curved, objects to shift position, and scan matching accuracy to degrade. The rotation distortion video is shown [here](https://drive.google.com/file/d/1KnMkAbJXtDpj2OJxOIoW3D4nRNeQGs_S/view?usp=sharing).
 
 In LIO-SAM, IMU measurements are used to estimate the sensor’s rotational motion during each scan and correct, or “undistort,” the point cloud before mapping. However, translational motion of the sensor is not fully accounted for. When the vehicle moves at high speed, this translation-induced distortion can become significant and may degrade mapping quality.
@@ -100,7 +100,7 @@ Before distortion correction:
 After distortion correction:
 ![](/assets/images/state-estimation/After_Undistortion.png)
 
-# Dynamic Obstacle Filtering
+## Dynamic Obstacle Filtering
 Dynamic obstacle filtering is applied after the initial map is constructed to remove points caused by moving objects, such as vehicles, pedestrians, or cyclists. These dynamic points may appear as ghost artifacts or inconsistent structures in the map because they do not belong to the static environment. By identifying and removing them from the generated point cloud map, the final map becomes cleaner, more reliable, and better suited for localization and navigation.
 
 A simple way to remove these dynamic obstacles is through manual selection. We use CloudCompare to edit and refine point cloud segments in the map. For example, in a map generated by LIO-SAM, many unwanted dynamic obstacles and outliers may remain. In CloudCompare, the Segment tool can be used to select the areas that need to be removed, as shown below:
@@ -116,7 +116,7 @@ After applying multiple segmentation steps across the map, merge all remaining s
 ![](/assets/images/state-estimation/DOF4.png)
 The white points represent the filtered dynamic obstacles, while the colored points represent the remaining static map.
 
-# Future Work
+## Future Work
 Several improvements can be added in future work. First, GNSS measurements can be integrated as an elevation drift constraint during mapping. This would help reduce vertical drift and improve map consistency, especially in large-scale outdoor environments.
 
 Second, better visualization tools can be developed to make the mapping and filtering results easier to inspect. Clear visualization of trajectory quality, point cloud alignment, removed dynamic objects, and map consistency would help with debugging and evaluation.