Safe Occlusion-Aware Manipulation

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This lecture presents a paper on safe, occlusion-aware manipulation for online object reconstruction in confined spaces, focusing on the challenges of object retrieval and reconstruction without prior knowledge of object models. The instructor discusses the development of combinatorial tools and a probabilistically complete framework to ensure safe reconstruction in scenarios with occlusions and partial observability.

Tomographic reconstruction is a type of multidimensional inverse problem where the challenge is to yield an estimate of a specific system from a finite number of projections. The mathematical basis for tomographic imaging was laid down by Johann Radon. A notable example of applications is the reconstruction of computed tomography (CT) where cross-sectional images of patients are obtained in non-invasive manner.

Iterative reconstruction refers to iterative algorithms used to reconstruct 2D and 3D images in certain imaging techniques. For example, in computed tomography an image must be reconstructed from projections of an object. Here, iterative reconstruction techniques are usually a better, but computationally more expensive alternative to the common filtered back projection (FBP) method, which directly calculates the image in a single reconstruction step.