Visual Odometry from Omnidirectional Images for Intelligent Transportation

In this article we use omnidirectional images obtained from equirectangular panoramas of Google Maps™ to estimate camera egomotion. The systems was also tested using a 360 camera. The goal is to provide an effective and accurate positioning system for indoor environments or in urban canyons where GPS signal could be absent. We reformulated classical Computer Vision geometrical constraints for pin-hole cameras, like epipolar and trifocal tensor, to omnidirectional cameras obtaining new and effective equations to accurately reconstruct the camera path using couples or triplets of omnidirectional images. Tests have been performed on straight and curved paths to validate the presented approaches.