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Visual-Inertial Odometry (VIO) utilizes an Inertial Measurement Unit (IMU) toovercome the limitations of Visual Odometry (VO). However, the VIO for vehiclesin large-scale outdoor environments still has some difficulties in estimatingforward motion with distant features. To solve these difficulties, we propose arobust VIO method based on the analysis of feature confidence in forward motionestimation using an IMU. We first formulate the VIO problem by using effectivetrifocal tensor geometry. Then, we infer the feature confidence by using themotion information obtained from an IMU and incorporate the confidence into theBayesian estimation framework. Experimental results on the public KITTI datasetshow that the proposed VIO outperforms the baseline VIO, and it alsodemonstrates the effectiveness of the proposed feature confidence analysis andconfidence-incorporated egomotion estimation framework.

Exploiting Feature Confidence for Forward Motion Estimation