Many different approaches can be taken to identify cones, this issue will detail progress into the development of online segmentation, following the implementation as described in Efficient Online Segmentation for Sparse 3D Laser
Scans.
Working Branch: feature/online_segment
Main Tasks
There are four main tasks to complete in order to successfully be able to use this algorithm to identify cones.
Conversion into Spherical Depth Image
As this algorithm relies on the use of spherical co-ordinates, the pointcloud must be cast to this format. In addition, a depth image must be constructed to simplify the comparison of neighboring ranges.
[x] Conversion to spherical co-ords
[x] Construction of depth image
Ground Removal
Following the process of the aforementioned paper, the detection and removal of the ground is the initial segmentation step. This involves creating a matrix of angle increments constructed from the columns of the depth image, smoothing said angles and conducting a search to identify points that belong to the ground.
[x] Search matrix to identify ground (Breadth First Search)
Segmentation
Following the removal of the ground, the remaining points can be segmented into individual objects by computing the angle
of a the plane connecting the points in consecutive columns.
[x] Produce matrix of angles
[x] Segment objects based on previous matrix
Classification
Once a list of segmented objects are found, this list must be classified to identify what is and isn't a cone. This will consist of a range of methods to be determined once the segmented clouds are computed.
Overview
Many different approaches can be taken to identify cones, this issue will detail progress into the development of online segmentation, following the implementation as described in Efficient Online Segmentation for Sparse 3D Laser Scans.
Working Branch:
feature/online_segment
Main Tasks
There are four main tasks to complete in order to successfully be able to use this algorithm to identify cones.
Conversion into Spherical Depth Image
As this algorithm relies on the use of spherical co-ordinates, the pointcloud must be cast to this format. In addition, a depth image must be constructed to simplify the comparison of neighboring ranges.
Ground Removal
Following the process of the aforementioned paper, the detection and removal of the ground is the initial segmentation step. This involves creating a matrix of angle increments constructed from the columns of the depth image, smoothing said angles and conducting a search to identify points that belong to the ground.
Segmentation
Following the removal of the ground, the remaining points can be segmented into individual objects by computing the angle of a the plane connecting the points in consecutive columns.
Classification
Once a list of segmented objects are found, this list must be classified to identify what is and isn't a cone. This will consist of a range of methods to be determined once the segmented clouds are computed.