Far feature detection using geometric uncertainty in visual-inertial navigation system

In this paper, we propose a new method that classifies the features in a visual-inertial navigation system. The far features provide inaccurate information on the position and velocity of a moving vehicle, and thus, the information provided by the far features should be used for attitude estimation without recovering the feature depths. In contrast, the information obtained from the near features can be used to obtain a reliable depth estimate because of the large parallax in the image plane. However, the criterion for labeling the features as near or far features is ambiguous. Previously, various geometric classification methods based on a stereo camera and measurement uncertainties have been reported. Herein, we present a criterion based on the geometric method using the MSCKF (Multi-State Constraint Kalman filter). Additionally, we define a new concept — depth uncertainty — as the criterion of feature classification in the MSCKF. Using this criterion, we can draw a limited range defined as the observable region. Implementation of this method can decrease the error caused by the low parallax of the feature. The proposed method is validated through simulations and experiments, showing a 12.7 % and 21.2 % decrease in the mean position error, respectively, using the far features classification. © ICROS 2021.