A Heteroscedastic Likelihood Model for Two-frame Optical Flow

TL;DR

This paper introduces a heteroscedastic likelihood model for two-frame optical flow that accurately captures uncertainty depending on image texture, improving visual odometry and sensor fusion for autonomous navigation.

Contribution

It proposes a new likelihood function for optical flow that models texture-dependent uncertainty, validated on popular algorithms and applied to visual odometry.

Findings

Close match with empirical error distributions across textures

Competitive visual odometry performance

Enables better sensor data fusion for navigation

Abstract

Machine vision is an important sensing technology used in mobile robotic systems. Advancing the autonomy of such systems requires accurate characterisation of sensor uncertainty. Vision includes intrinsic uncertainty due to the camera sensor and extrinsic uncertainty due to environmental lighting and texture, which propagate through the image processing algorithms used to produce visual measurements. To faithfully characterise visual measurements, we must take into account these uncertainties. In this paper, we propose a new class of likelihood functions that characterises the uncertainty of the error distribution of two-frame optical flow that enables a heteroscedastic dependence on texture. We employ the proposed class to characterise the Farneback and Lucas Kanade optical flow algorithms and achieve close agreement with their respective empirical error distributions over a wide range of texture in a simulated environment. The utility of the proposed likelihood model is demonstrated in a visual odometry ego-motion study, which results in performance competitive with contemporary methods. The development of an empirically congruent likelihood model advances the requisite tool-set for vision-based Bayesian inference and enables sensor data fusion with GPS, LiDAR and IMU to advance robust autonomous navigation.