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While an exciting diversity of new imaging devices is emerging that coulddramatically improve robotic perception, the challenges of calibrating andinterpreting these cameras have limited their uptake in the robotics community.In this work we generalise techniques from unsupervised learning to allow arobot to autonomously interpret new kinds of cameras. We consider emergingsparse light field (LF) cameras, which capture a subset of the 4D LF functiondescribing the set of light rays passing through a plane. We introduce ageneralised encoding of sparse LFs that allows unsupervised learning ofodometry and depth. We demonstrate the proposed approach outperformingmonocular and conventional techniques for dealing with 4D imagery, yieldingmore accurate odometry and depth maps and delivering these with metric scale.We anticipate our technique to generalise to a broad class of LF and sparse LFcameras, and to enable unsupervised recalibration for coping with shifts incamera behaviour over the lifetime of a robot. This work represents a firststep toward streamlining the integration of new kinds of imaging devices inrobotics applications.

Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras