Exploiting Feature Confidence for Forward Motion Estimation

TL;DR

This paper introduces a robust visual-inertial odometry method that leverages feature confidence analysis and trifocal tensor geometry to improve forward motion estimation in large-scale outdoor environments.

Contribution

It presents a novel VIO approach that integrates feature confidence derived from IMU data into a Bayesian framework, enhancing motion estimation accuracy.

Findings

Outperforms baseline VIO on KITTI dataset

Effective feature confidence analysis improves robustness

Confidence-incorporated egomotion estimation enhances accuracy

Abstract

Visual-Inertial Odometry (VIO) utilizes an Inertial Measurement Unit (IMU) to overcome the limitations of Visual Odometry (VO). However, the VIO for vehicles in large-scale outdoor environments still has some difficulties in estimating forward motion with distant features. To solve these difficulties, we propose a robust VIO method based on the analysis of feature confidence in forward motion estimation using an IMU. We first formulate the VIO problem by using effective trifocal tensor geometry. Then, we infer the feature confidence by using the motion information obtained from an IMU and incorporate the confidence into the Bayesian estimation framework. Experimental results on the public KITTI dataset show that the proposed VIO outperforms the baseline VIO, and it also demonstrates the effectiveness of the proposed feature confidence analysis and confidence-incorporated egomotion estimation framework.