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

A Light Field Front-end for Robust SLAM in Dynamic Environments