Abstract
Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the “windowed pose optimization network” is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-of-the-art related works which shows the novelty of the proposed technique.
Highlights
Autonomous vehicles, including unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV), and unmanned underwater vehicles (UUV), are increasingly used to explore the different difficult and dangerous environments to minimize human interaction
The method of performing the continuous localization using cameras or visual-only sensors is known as visual odometry (VO)
It was clear that increasing the data augmentation over a specific point degrades the performance
Summary
Autonomous vehicles, including unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV), and unmanned underwater vehicles (UUV), are increasingly used to explore the different difficult and dangerous environments to minimize human interaction. Estimating the ego-motion or continuous localization of the robot in an environment is a fundamental long-standing challenge in autonomous navigation. Continuous localization is performed using sensors, such as global positioning systems (GPS), inertial sensors, and wheel encoders for ground robots. Traditional methods suffer from accumulated drift and GPS is constrained to only open environments. Recent studies expressed immense interest to perform the localization task using cameras due to vast information. The method of performing the continuous localization using cameras or visual-only sensors is known as visual odometry (VO). The applications of visual odometry vary widely from scene reconstruction [1], indoor localization [2], biomedical applications [3], and virtual and augmented reality [4] to self-driving vehicles [5]
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