I am working with 3D point clouds acquired from an object and I need to align them in a single global point cloud. I am having an hard time in understanding the difference between SLAM and registration. Especially since both of them can implement ICP The point clouds have been acquired in spatial and temporal order and hav extended overlapping area; therefore I should could SLAM for aligning them. Anyone can clarify this point to me?
Thanks! anna
Based on your question it sounds like you are working with a depth sensor of some kind moving through an environment. You would like to create a consistent map or point cloud with this sensor. I would have commented this, but my reputation is too low at the moment.
Registration just refers to the alignment of two measurements through some transformation. For image registration this is typically when you find some transformation whether it be a simple translation or an affine warp between two images which makes them 'look' similar. Point cloud registration typically refers to finding a rotation and translation which aligns two point clouds.
SLAM, as you probably know, refers to simultaneous localization and mapping. The goal of SLAM is to find the sensors motion through a scene, and map the scene at the same time.
I think the reason you are having a hard time seeing the difference between the two is because, for your application, registration is a way of accomplishing a simple form of SLAM. The reason for this is because ICP essentially is finding the relative transformation of your depth sensor between two measurements. This is acts as odometry for your sensor.
However, registration is not necessarily going to give you a relative sensor pose in all applications. For example a KLT tracker is a form of simple image registration, but it does not give the relative transformation of two cameras directly.
I hope this clears it up.
