Semi-parametric models for visual odometry

This paper introduces a novel framework for estimating the motion of a robotic car from image information, a scenario widely known as visual odometry. Most current monocular visual odometry algorithms rely on a calibrated camera model and recover relative rotation and translation by tracking image features and applying geometrical constraints. This approach has some drawbacks: translation is recovered up to a scale, it requires camera calibration which can be tricky under certain conditions, and uncertainty estimates are not directly obtained. We propose an alternative approach that involves the use of semi-parametric statistical models as means to recover scale, infer camera parameters and provide uncertainty estimates given a training dataset. As opposed to conventional non-parametric machine learning procedures, where standard models for egomotion would be neglected, we present a novel framework in which the existing parametric models and powerful non-parametric Bayesian learning procedures are combined. We devise a multiple output Gaussian Process (GP) procedure, named Coupled GP, that uses a parametric model as the mean function and a non-stationary covariance function to map image features directly into vehicle motion. Additionally, this procedure is also able to infer joint uncertainty estimates (full covariance matrices) for rotation and translation. Experiments performed using data collected from a single camera under challenging conditions show that this technique outperforms traditional methods in trajectories of several kilometers.

Authors

• Vitor Guizilini
• Fabio Ramos 0001

Keywords

• Cameras
• Optical imaging
• Visualization
• Vehicles
• Optical sensors
• Calibration
• Training
• Semiparametric Model
• Visual Odometry
• Model Parameters
• Machine Learning
• Training Dataset
• Covariance Matrix
• Image Features
• Gaussian Process
• Learning Procedure
• Mean Function
• Geometric Constraints
• Multiple Outputs
• Covariance Function
• Single Camera
• Vehicle Motion
• Camera Calibration
• Ego-motion
• Angular Velocity
• Unmanned Aerial Vehicles
• Geometric Model
• Optical Flow
• Iterative Closest Point Algorithm
• Feature Matching
• Iterative Closest Point
• Linear Velocity
• Simultaneous Localization And Mapping
• Camera Motion
• SIFT Features
• Pair Of Frames
• Need For Calibration