From Local Understanding to Global Regression in Monocular Visual Odometry

Abstract

The most significant part of any autonomous intelligent robot is the localization module that gives the robot knowledge about its position and orientation. This knowledge assists the robot to move to the location of its desired goal and complete its task. Visual Odometry (VO) measures the displacement of the robots’ camera in consecutive frames which results in the estimation of the robot position and orientation. Deep Learning, nowadays, helps to learn rich and informative features for the problem of VO to estimate frame-by-frame camera movement. Recent Deep Learning-based VO methods train an end-by-end network to solve VO as a regression problem directly without visualizing and sensing the label of training data in the training procedure. In this paper, a new approach to train Convolutional Neural Networks (CNNs) for the regression problems, such as VO, is proposed. The proposed method first changes the problem to a classification problem to learn different subspaces with similar observations. After solving the classification problem, the problem converts to the original regression problem to solve using the knowledge achieved by solving the classification problem. This approach helps CNN to solve regression problem globally in a local domain learned in the classification step, and improves the performance of the regression module for approximately 10%.

A monocular vision positioning and tracking system based on deep neural network

“…This method achieves a good effect and can be applied to outdoor positioning. Compared with traditional positioning methods, the positioning algorithm based on a neural network can obtain more accurate positioning and better generalization performance through end-to-end learning without establishing complex geometric models, which is a research hotspot of current visual positioning algorithms [12]. The above localization algorithms based on neural networks have achieved good localization results, but there are still some problems, such as it is difficult to achieve high positioning accuracy for dynamic objects, and the process of obtaining or labelling training data is cumbersome.…”

A Monocular vision positioning and tracking system based on deep neural network

“…It could be used for accurate positioning of multiple scenes, but the algorithm needs good initialization and is susceptible to changes in perspective. Compared with traditional positioning methods, positioning algorithm based on neural network can obtain more accurate positioning and better generalization performance through end-to-end learning without establishing complex geometric models, which is a research hotspot of current visual positioning algorithms [12]. The above localization algorithms based on neural networks have achieved good localization results, but there are still some problems, such as it is difficult to achieve high positioning accuracy for dynamic objects, and the process of obtaining or labeling training data is cumbersome.…”