[✨FEAT✨]: Cone mapping

[Jack17432]

Is there an existing enhancement request for this?

• I have searched the existing feature requests

Description

Map of all cones detected

Additional Information

No response

[dyu056-fsae]

Plan:
1: Assuming perfect sensor and assume that the cone detection team provides the distances and angle from the front for all of the cones. Complete the mapping project. (Due date likely June)
2: Improve the existing mapping algorithm with perfect sensor by considering the sensor imperfectness (kalman filter or other thing)
(Due date likely July)

[Jack17432]

For the plan I would like somthing more spesific and a plan of actions an example might be :

We are going to first make 3 test data set by hand then we will try to see if x algo will be effective and then compare it to y, we are going to do this by making a node called z that is in package a yada yada

this is a good basic outline but a plan should be a plan of action so that the team and you guys know what are the steps you have to do in order to compleate this

[dyu056-fsae]

1: Use python script to generate test data, use publisher to simulate cone detection output and use subscriber to receive output

2: Create a map of the track using a mapping algorithm such as occupancy grid mapping or SLAM.

3: Use the 3D world coordinates of the cone to determine its position in relation to the robot's current pose and output a ROS PoseWithCovariance message with the cone's position and orientation.

4: Continuously update the map with new cone detections and publish the updated map to a separate topic.

5: Attach the color (int) of the cone to the ROS message.

6: Publish the ROS message to a designated topic.

7: Test the algorithm on a real-world scenario to ensure it is robust and accurate enough for deployment. Monitor the performance of the algorithm, tweak parameters as necessary, add filtering method for noises, and optimize for speed and accuracy (Iterative process). Next possible iteration might be:

• Add / adjust noise to simulated data output
• Add filtering algorithm
• Test again

[dyu056]

Merged Kalman filter cone mapping version, now exploring particle filter version