A computer implemented method of estimating a pose of a device, the method comprising: identifying a similar key frame that is similar to a current frame collected by the device from a key frame set, based on a vector distance between the similar key frame and the current frame; obtaining data-related information between image frames based on a feature matching relationship between the current frame and the similar key frame; obtaining the pose of the device based on the data-related information; characterized in that the similar key frame is obtained from the key frame set, through an image feature extraction model that is trained using an original image, a modified image that is obtained by rotating or flipping the image, and a global feature and a local feature that are extracted from each of the original image and the modified image; and further comprising: obtaining a first local feature and a first global feature of the original image through the image feature extraction model; obtaining a second local feature of the modified image through a target convolutional neural network, and performing a conjugate rotation flip on the second local feature of the modified image to obtain a conjugate rotation-flipped local feature of the modified image; obtaining a local feature pair based on the first local feature of the original image and the conjugate rotation-flipped local feature of the modified image; and updating parameters of the image feature extraction model based on the local feature pair and the first global feature; wherein the conjugate rotation flip is an operation conjugate to the operation that was performed to obtain the modified image. identifying a similar key frame that is similar to a current frame collected by the device from a key frame set, based on a vector distance between the similar key frame and the current frame; obtaining data-related information between image frames based on a feature matching relationship between the current frame and the similar key frame; obtaining the pose of the device based on the data-related information; characterized in that the similar key frame is obtained from the key frame set, through an image feature extraction model that is trained using an original image, a modified image that is obtained by rotating or flipping the image, and a global feature and a local feature that are extracted from each of the original image and the modified image; and further comprising: obtaining a first local feature and a first global feature of the original image through the image feature extraction model; obtaining a second local feature of the modified image through a target convolutional neural network, and performing a conjugate rotation flip on the second local feature of the modified image to obtain a conjugate rotation-flipped local feature of the modified image; obtaining a local feature pair based on the first local feature of the original image and the conjugate rotation-flipped local feature of the modified image; and updating parameters of the image feature extraction model based on the local feature pair and the first global feature; wherein the conjugate rotation flip is an operation conjugate to the operation that was performed to obtain the modified image. obtaining a first local feature and a first global feature of the original image through the image feature extraction model; obtaining a second local feature of the modified image through a target convolutional neural network, and performing a conjugate rotation flip on the second local feature of the modified image to obtain a conjugate rotation-flipped local feature of the modified image; obtaining a local feature pair based on the first local feature of the original image and the conjugate rotation-flipped local feature of the modified image; and updating parameters of the image feature extraction model based on the local feature pair and the first global feature; wherein the conjugate rotation flip is an operation conjugate to the operation that was performed to obtain the modified image.