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Self-supervised monocular depth estimation has been widely investigated toestimate depth images and relative poses from RGB images. This framework isattractive for researchers because the depth and pose networks can be trainedfrom just time sequence images without the need for the ground truth depth andposes.  In this work, we estimate the depth around a robot (360 degree view) usingtime sequence spherical camera images, from a camera whose parameters areunknown. We propose a learnable axisymmetric camera model which acceptsdistorted spherical camera images with two fisheye camera images. In addition,we trained our models with a photo-realistic simulator to generate ground truthdepth images to provide supervision. Moreover, we introduced loss functions toprovide floor constraints to reduce artifacts that can result from reflectivefloor surfaces. We demonstrate the efficacy of our method using the sphericalcamera images from the GO Stanford dataset and pinhole camera images from theKITTI dataset to compare our method's performance with that of baseline methodin learning the camera parameters.

Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model