Add design doc for actual_arrel_segmentationn issue #569

documents/design/actual_barrel_segmentation.md

@@ -0,0 +1,46 @@
+# Actual barrel segmentation 
+
+*Issue #569*
+
+**Author:**
+- Zhiyan Zhou (Frank)
+
+## The Problem
+Cluster points from non-ground pointcloud into different groups representing different objects. We can potentially use the information for things like SLAM.
+End goal will be separating barrels into individual groups and extracting information such as position, Num of barrels etc.
+
+## Proposed Solution
+- Create a subscriber that suscribes to the filtered non-ground data.
+- Use the clustering algorithm inside PCL.
+- (If necessary)Program a clustering algorithm that take in a pointcloud and returns a clustered pointcloud.
+- Visualized clustering result.
+- Tune the parameter of the optimal algorithm if necessary.
+- Use the clusters to calculate metrics (ex: position, num of barrels/) 
+- Publish the metrics to the corresponding topic. 
+
+## Questions & Research
+- What are the common clustering algorithms?
+- Any known implementations in ROS/C++?
+- /igvc-software/documents/research/barrel_detection.md
+- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338962/pdf/sensors-19-00172.pdf
+
+## Overall Scope
+
+### Affected Packages
+- /igvc-software/igvc_perception/src/pointcloud_filter/
+
+### Schedule
+
+Subtask 1 (12/24/2019): Program a subscriber that subscribes pointcloud data.
+
+Subtask 2 (2-3 week into 2019 Spring): Use the clustering algorithm inside PCL to cluster points.
+
+Subtask 3 (/): (If necessary)Go over the paper listed: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338962/pdf/sensors-19-00172.pdf
+
+Subtask 4 (/): (If necessary) Implement a DBSCAN algorithm 
+
+Subtask 5 (/): (If necessary) Test the original DBSCAN algorithm under a known distance
+
+Subtask 6 (/): (If necessary) Work on Auto Hyperparameter Tuning. 
+
+Subtask 7 (3-4 weeks into 2019 Spring): Test and tune the algorithm under simulated data.