Block Kalman filter with linear computational complexity for integrated visual-inertial navigation systems

As the use of computer vision in vehicle navigation systems increases, there is a growing need for computationally efficient optimal filtering algorithms that can jointly process measurements from inertial sensors and a large number of visual information sources. Contribution. This paper proposes a new modification of the Kalman filter (the block Kalman filter) with linear computational complexity in the number of measurement information sources. A numerically stable version of the algorithm is derived using LDL-factorization of covariance matrices. Purpose of the study. The aim is to develop an LDL-factorized block Kalman filter algorithm with linear computational complexity with respect to the number of information sources in the measurement system and demonstrate its applicability in a integrated visualinertial navigation system.