Outlier rejection for visual odometry using parity space methods

Typically, random sample consensus (RANSAC) approaches are used to perform outlier rejection for visual odometry, however the use of RANSAC can be computationally expensive. The parity space approach (PSA) provides methodology to perform computationally efficient consistency checks for observations, without having to explicitly compute the system state. This work presents two outlier based rejection techniques, Group Parity Outlier Rejection and Parity Initialized RANSAC, which use the parity space approach to perform rapid outlier rejection. Experiments demonstrate the proposed approaches are able to compute solutions with increased accuracy and improved run-time when compared to RANSAC.