Information sparsification for visual-inertial odometry by manipulating Bayes tree

Abstract

A new information sparsification method is developed for visual-inertial odometry (VIO) used for exploratory unmanned aerial vehicles (UAV's). While most of the existing methods are designed for global (and thus postprocessing) bundle adjustment (BA) problems, our method is specifically designed to run incrementally on a local BA formulation in real time. The majority of the existing state-of-the-art methods are designed for pose graph optimization (PGO). Unlike PGO problems where the only variables to be optimized are robot poses, a combination of pose, velocity, and inertial measurement unit (IMU) bias is used in VIO. Existing approaches to sparsification cannot be directly applied to this formulation. Therefore, we design a trick to split the dense information matrix into two parts: IMU information and purely visual information and by doing so, existing methods of sparsification can be reused and applied to the purely visual information, which accounts for most of the nonzero entries within the dense information matrix. In addition, we design a new way of summarizing the dense information matrix: using Bayes tree in an incremental smoothing framework for fast data retrieval. Results using public benchmark dataset EuRoC show that the marginalized and sparsified smoother achieves the primary goal of power saving in terms of shortened pipeline cycle time while maintaining absolute trajectory accuracies comparable to that of the complete formulation and better than those offered by fellow sparsification solutions.