A co-processor design to accelerate sequential monocular SLAM EKF process

Abstract Extended Kalman Filter (EKF) is a non-linear state estimation technique which is used to produce values that close to the true value when given with measurement containing noise and other inaccuracies. In Monocular Simultaneous Localization and Mapping (SLAM), EKF is used to estimate position and motion information. In this paper, Monocular SLAM software implementation on a general purpose computer is studied to find the most time consuming part of the estimation program. The analysis concentrates on the Monocular SLAM EKF estimation process which involves prediction, measurement prediction, matching and update. For this purpose, a form of dynamic programming analysis tool called software profiling is utilized to determine which section of the estimation program demands the highest processing time. Based on the analysis, it is found that EKF “matching” process contribute to the highest computation time. The reason behind the time-consuming process is because for every predicted feature in the matching stage, the acceptance region and their cross correlation have to be calculated. In a typical general purpose computer software implementation, the processing is limited to sequences of operations (i.e. sequential processing). Such implementation will delay the next process until the prior process completed. However, further analysis conducted in this paper shows that each feature does not depend on the prior process and can be processed individually. This would allow several features to be processed simultaneously to improve the execution speed. Therefore, an FPGA pipelined and parallel processing architecture is proposed.