Visual-inertial odometry with depth sensing using a multi-state constraint Kalman filter

Abstract

The goal of visual inertial odometry (VIO) is to estimate a moving vehicle's trajectory using inertial measurements and observations, obtained by a camera, of naturally occurring point features. One existing VIO estimation algorithm for use with a monocular system, is the multi-state constraint Kalman filter (MSCKF), proposed by Mourikis and Li [34, 29]. The way the MSCKF uses feature measurements drastically improves its performance, in terms of consistency, observability, computational complexity and accuracy, compared to other VIO algorithms [29]. For this reason, the MSCKF is chosen as the basis for the estimation algorithm presented in this thesis. A VIO estimation algorithm for a system consisting of an IMU, a monocular camera and a depth sensor is presented in this thesis. The addition of the depth sensor to the monocular camera system produces three-dimensional feature locations rather than two-dimensional locations. Therefore, the MSCKF algorithm is extended to use the extra information. This is accomplished using a model proposed by Dryanovski et al. that estimates the 3D location and uncertainty of each feature observation by approximating it as a multivariate Gaussian distribution [11]. The extended MSCKF algorithm is presented and its performance is compared to the original MSCKF algorithm using real-world data obtained by flying a custom-built quadrotor in an indoor office environment.