Patent · US Active

Method for training a neural convolutional network for determining a localization pose

US11315279B2 · kind B2 · utility

0Cited by

1References

15Claims

0Family size

Assignee

Robert Bosch GmbH · DE

Inventors

Carsten Hasberg · Ilsfeld, DE
Piyapat Saranrittichai · Nuremberg, DE
Tayyab Naseer · London, GB

Key dates

Filing date	Sep 21, 2020
Grant date	Apr 26, 2022
Priority date	—
Expiry date	Nov 25, 2040

Classification

Technology area (CPC G)Physics
CPC primaryG06T2207/30252
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

A method for training a neural convolutional network for determining, with the aid of the neural convolutional network, a localization pose of a mobile platform using a ground image. Using a first multitude of aerial image training cycles, each aerial image training cycle includes: providing a reference pose of the mobile platform; and providing an aerial image of the environment of the mobile platform in the reference pose; using the aerial image as an input signal of the neural convolutional network; determining the respective localization pose with the aid of an output signal of the neural convolutional network; and adapting the neural convolutional network to minimize a deviation of the respective localization pose determined using the respective aerial image from the respective reference pose.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.