Improving Learningbased Egomotion Estimation with Homomorphismbased Losses and Drift Correction Visual odometry is an essential problem for mobile robots. Traditional methods for solving VO mostly utilize geometric optimization. While capable of achieving high accuracy, these methods require accurate sensor calibration and complicated parameter tuning to work well in practice. With the rise of deep learning, there has been increased interest in the endtoend, learningbased methods for VO, which have the potential to improve robustness. However, learningbased methods for VO so far are less accurate than geometric methods. We argue that one of the main issues is that the current egomotion estimation task is different from other problems where deep learning has been successful such as object detection. We define a novel cost function for learningbased VO considering the mathematical properties of the group homomorphism. In addition to the standard L2 loss, we incorporate losses based on the identity, inverse and closure properties of SE(3) rigid motion. Furthermore, we propose to reduce the VO drift by estimating the drivable regions using semantic segmentation and incorporate this information into a pose graph optimization. Experiments on KITTI datasets show that the novel cost function can improve egomotion estimation compared to the stateoftheart and the drivable regionbased correction further reduces the VO drift.

PoseConvGRU: A Monocular Approach for Visual Egomotion Estimation by Learning Abstract   Visual egomotion estimation is one of the longstanding problems which estimates the movement of cameras from images. Learning based egomotion estimation methods have seen an increasing attention since its desirable properties of robustness to image noise and camera calibration independence. In this work, we propose a datadriven approach of learning based visual egomotion estimation for a monocular camera. We use an endtoend learning approach in allowing the model to learn a map from input image pairs to the corresponding egomotion, which is parameterized as 6DoF transformation matrix. We introduce a twomodule Longterm Recurrent Convolutional Neural Networks called PoseConvGRU. The featureencoding module encodes the shortterm motion feature in an image pair, while the memorypropagating module captures the longterm motion feature in the consecutive image pairs. The visual memory is implemented with convolutional gated recurrent units, which allows propagating information over time. At each time step, two consecutive RGB images are stacked together to form a 6channel tensor for featureencoding module to learn how to extract motion information and estimate poses. The sequence of output maps is then passed through the memorypropagating module to generate the relative transformation pose of each image pair. In addition, we have designed a series of data augmentation methods to avoid the overfitting problem and improve the performance of the model when facing challengeable scenarios such as highspeed or reverse driving. We evaluate the performance of our proposed approach on the KITTI Visual Odometry benchmark and Malaga 2013 Dataset. The experiments show a competitive performance of the proposed method to the stateoftheart monocular geometric and learning methods and encourage further exploration of learningbased methods for the purpose of estimating camera egomotion even though geometrical methods demonstrate promising results.