  There is a general expectation that robots should operate in urban
environments often consisting of potentially dynamic entities including people,
furniture and automobiles. Dynamic objects pose challenges to visual SLAM
algorithms by introducing errors into the front-end. This paper presents a
Light Field SLAM front-end which is robust to dynamic environments. A Light
Field captures a bundle of light rays emerging from a single point in space,
allowing us to see through dynamic objects occluding the static background via
Synthetic Aperture Imaging(SAI). We detect apriori dynamic objects using
semantic segmentation and perform semantic guided SAI on the Light Field
acquired from a linear camera array. We simultaneously estimate both the depth
map and the refocused image of the static background in a single step
eliminating the need for static scene initialization. The GPU implementation of
the algorithm facilitates running at close to real time speeds of 4 fps. We
demonstrate that our method results in improved robustness and accuracy of pose
estimation in dynamic environments by comparing it with state of the art SLAM
algorithms.
