Abstract: A computer-implemented method to improve scale consistency and/or scale awareness in a model of self-supervised depth and ego-motion prediction neural networks processing a video stream of monocular images, wherein complementary GPS coordinates synchronized with the images are used to calculate a GPS to scale loss to enforce the scale-consistency and/or -awareness on the monocular self-supervised ego-motion and depth estimation. A relative weight assigned to the GPS to scale loss exponentially increases as training progresses. The depth and ego-motion prediction neural networks are trained using an appearance-based photometric loss between real and synthesized target images, as well as a smoothness loss on the depth predictions.

Abstract: Systems arranged to implement methods for positioning a semantic landmark in an image from the real world during a continuous motion of a monocular camera providing said image, using in combination image information from the camera and GPS information, wherein the camera parameters are unknown a priori and are estimated in a self-calibration step, wherein in a subsequent step positioning of the landmarks is completed using one of camera ego motion and depth estimation.

Abstract: A computer-implemented method of self-supervised learning in neural network for scene understanding in autonomously moving vehicles wherein the method to estimate the ego-motion and the intrinsics (focal lengths and principal point) robustly in a unified manner from a pair of input overlapping images captured from a monocular camera, within a self-supervised monocular depth and ego-motion estimation problem by including multi-head self-attention modules within a transformer architecture.

Abstract: A computer-implemented method to improve scale consistency and/or scale awareness in a model of self-supervised depth and ego-motion prediction neural networks processing a video stream of monocular images, wherein complementary GPS coordinates synchronized with the images are used to calculate a GPS to scale loss to enforce the scale-consistency and/or -awareness on the monocular self-supervised ego-motion and depth estimation. A relative weight assigned to the GPS to scale loss exponentially increases as training progresses. The depth and ego-motion prediction neural networks are trained using an appearance-based photometric loss between real and synthesized target images, as well as a smoothness loss on the depth predictions.

Abstract: Systems arranged to implement methods for positioning a semantic landmark in an image from the real world during a continuous motion of a monocular camera providing said image, using in combination image information from the camera and GPS information, wherein the camera parameters are unknown a priori and are estimated in a self-calibration step, wherein in a subsequent step positioning of the landmarks is completed using one of camera ego motion and depth estimation.