feat(#130): renamed files and linked them properly

doc/03_research/02_perception/Readme.md

@@ -7,4 +7,5 @@ This folder contains all the results of research on perception:
   * [First implementation plan](./03_first_implementation_plan.md)
 * **PAF23**
   * [Pylot Perception](./04_pylot.md)
-  * [PAF_21_1 Perception](./05_Research_PAF21-Perception.md)
+  * [PAF_21_2 Perception](./05_Research_PAF21-Perception.md)
+  * [PAF_21_1_Perception](./06_paf_21_1_perception.md)

doc/06_perception/10_sensor_filter_debug.md → doc/06_perception/07_sensor_filter_debug.md

@@ -1,6 +1,6 @@
 # sensor_filter_debug.py
 
-**Summary:** [sensor_filter_debug.py](.../code/perception/src/sensor_filter_debug.py):
+**Summary:** [sensor_filter_debug.py](../../code/perception/src/sensor_filter_debug.py):
 
 The sensor_filter_debug node is responsible for collecting sensor data from the IMU and GNSS and process the data in such a way, that it shows the errors between the real is-state and the measured state.
 The data is the shown in multiple rqt_plots.
@@ -33,9 +33,9 @@ Robert Fischer
 
 ## Getting started
 
-Uncomment the sensor_filter_debug.py node in the [perception.launch](.../code/perception/launch/perception.launch) to start the node.
+Uncomment the sensor_filter_debug.py node in the [perception.launch](../../code/perception/launch/perception.launch) to start the node.
 You can also uncomment the rqt_plots that seem useful to you, or create your own ones from the data published.
-You have to add the following sensors to the sensors inside the [dev_objects.json](.../code/agent/config/dev_objects.json):
+You have to add the following sensors to the sensors inside the [dev_objects.json](../../code/agent/config/dev_objects.json):
 
 ```json
 {
@@ -101,7 +101,7 @@ Right now only the IMU and the GNSS sensor are available for debug.
 Debug for the RADAR and LIDAR hasn't been implemented yet.
 
 An Example of Location Error Output can be seen here:
-![Distance from current_pos to ideal_gps_pos (blue) and to carla_pos (red)](.../doc/00_assets/gnss_ohne_rolling_average.png)
+![Distance from current_pos to ideal_gps_pos (blue) and to carla_pos (red)](../00_assets/gnss_ohne_rolling_average.png)
 
 ### Inputs
 

doc/06_perception/11_kalman_filter.md → doc/06_perception/08_kalman_filter.md

@@ -1,6 +1,6 @@
 # Kalman Filter
 
-**Summary:** [kalman_filter.py](.../code/perception/src/kalman_filter.py):
+**Summary:** [kalman_filter.py](../../code/perception/src/kalman_filter.py):
 
 The Kalman Filter node is responsible for filtering the location and heading data, by using an IMU and GNSS sensor together with the carla speedometer.
 
@@ -38,7 +38,7 @@ Robert Fischer
 Right now the Node does not work correctly. It creates topics to publish to, but doesn't yet.
 This will be fixed in [#106](https://github.com/una-auxme/paf23/issues/106)
 
-Uncomment the kalman_filter.py node in the [perception.launch](.../code/perception/launch/perception.launch) to start the node.
+Uncomment the kalman_filter.py node in the [perception.launch](../../code/perception/launch/perception.launch) to start the node.
 You can also uncomment the rqt_plots that seem useful to you.
 No extra installation needed.
 
@@ -60,7 +60,7 @@ Stackoverflow and other useful sites:
 [4](https://github.com/Janudis/Extended-Kalman-Filter-GPS_IMU)
 
 This script implements a Kalman Filter. It is a recursive algorithm used to estimate the state of a system that can be modeled with linear equations.
-This Kalman Filter uses the location provided by a GNSS sensor (by using the current_pos provided by the [Position Publisher Node](.../code/perception/src/Position_Publisher_Node.py))
+This Kalman Filter uses the location provided by a GNSS sensor (by using the current_pos provided by the [Position Publisher Node](../../code/perception/src/Position_Publisher_Node.py))
 the orientation and angular velocity provided by the IMU sensor and the current speed in the headed direction by the Carla Speedometer.
 
 ```Python

doc/06_perception/Readme.md

@@ -4,6 +4,16 @@ This folder contains further documentation of the perception components.
 
 ## 1. [Dataset generator](./01_dataset_generator.md)
 
-## 10. [sensor_filter_debug.py](./10_sensor_filter_debug.md)
+## 2. [Dataset Structure](./02_dataset_structure.md)
 
-## 11. [Kalman Filter](./11_kalman_filter.md)
+## 3. [Lidar Distance Utility](./03_lidar_distance_utility.md)
+
+## 4. [Efficient PS](./04_efficientps.md)
+
+## 5. [Autoware Perception](./05-autoware-perception.md)
+
+## 6. [Vision Node](./06_vision_node.md)
+
+## 7. [sensor_filter_debug.py](./07_sensor_filter_debug.md)
+
+## 8. [Kalman Filter](./08_kalman_filter.md)