  There is a general expectation that robots should operate in environments
that consist of static and dynamic entities including people, furniture and
automobiles. These dynamic environments pose challenges to visual simultaneous
localization and mapping (SLAM) algorithms by introducing errors into the
front-end. Light fields provide one possible method for addressing such
problems by capturing a more complete visual information of a scene. In
contrast to a single ray from a perspective camera, Light Fields capture a
bundle of light rays emerging from a single point in space, allowing us to see
through dynamic objects by refocusing past them.
  In this paper we present a method to synthesize a refocused image of the
static background in the presence of dynamic objects that uses a light-field
acquired with a linear camera array. We simultaneously estimate both the depth
and the refocused image of the static scene using semantic segmentation for
detecting dynamic objects in a single time step. This eliminates the need for
initializing a static map . The algorithm is parallelizable and is implemented
on GPU allowing us execute it at close to real time speeds. We demonstrate the
effectiveness of our method on real-world data acquired using a small robot
with a five camera array.
