Probabilistic Depth Image Registration incorporating Nonvisual Information
This work addresses object registration in robotics applications, such as manipulation tasks, by integrating nonvisual data, but it is incremental as it builds on Bayesian frameworks and existing registration techniques.
The paper tackles the problem of object modeling and tracking by deriving a probabilistic registration algorithm that incorporates nonvisual information and considers both object observations and empty space, resulting in favorable performance compared to existing methods like PCL implementations of feature mapping and ICP, especially when nonvisual information is available.
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL implementations of feature mapping and ICP, especially if nonvisual information is available.