35 research outputs found
RPNet: an End-to-End Network for Relative Camera Pose Estimation
This paper addresses the task of relative camera pose estimation from raw
image pixels, by means of deep neural networks. The proposed RPNet network
takes pairs of images as input and directly infers the relative poses, without
the need of camera intrinsic/extrinsic. While state-of-the-art systems based on
SIFT + RANSAC, are able to recover the translation vector only up to scale,
RPNet is trained to produce the full translation vector, in an end-to-end way.
Experimental results on the Cambridge Landmark dataset show very promising
results regarding the recovery of the full translation vector. They also show
that RPNet produces more accurate and more stable results than traditional
approaches, especially for hard images (repetitive textures, textureless
images, etc). To the best of our knowledge, RPNet is the first attempt to
recover full translation vectors in relative pose estimation
DPC-Net: Deep Pose Correction for Visual Localization
We present a novel method to fuse the power of deep networks with the
computational efficiency of geometric and probabilistic localization
algorithms. In contrast to other methods that completely replace a classical
visual estimator with a deep network, we propose an approach that uses a
convolutional neural network to learn difficult-to-model corrections to the
estimator from ground-truth training data. To this end, we derive a novel loss
function for learning SE(3) corrections based on a matrix Lie groups approach,
with a natural formulation for balancing translation and rotation errors. We
use this loss to train a Deep Pose Correction network (DPC-Net) that predicts
corrections for a particular estimator, sensor and environment. Using the KITTI
odometry dataset, we demonstrate significant improvements to the accuracy of a
computationally-efficient sparse stereo visual odometry pipeline, that render
it as accurate as a modern computationally-intensive dense estimator. Further,
we show how DPC-Net can be used to mitigate the effect of poorly calibrated
lens distortion parameters.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the
IEEE International Conference on Robotics and Automation (ICRA'18), Brisbane,
Australia, May 21-25, 201