A Consistent Parallel Estimation Framework for Visual-Inertial SLAM

In this article, we revisit the optimal fusion of visual and inertial information from a monocular camera and an inertial measurement unit and propose a novel parallel visual-inertial simultaneous localization and mapping (SLAM) estimation framework in favor of the multithread computation on a single CPU. We start modeling the SLAM problem with a Bayesian batch estimator, and then split it into two submodules, localization and mapping, of different scales and processing rates, however, can thus run concurrently. The estimation consistency is taken into account in decoupling the two submodules so that when loop closure occurs the localization accuracy can seamlessly benefit from the mapping result via online global optimization, which distinguishes our solution from the others. To this end, we design the corresponding front-end and back-end to consistently solve localization and mapping in parallel, especially the hybrid robocentric and world-centric formulations are used for modeling the respective problems. We also demonstrate the effectiveness of the proposed method using both the synthetic data generated for Monte-Carlo simulations and diverse real datasets acquired in highly-dynamic, long-term, and large-scale SLAM scenarios. Simulation results validate the significantly improved consistency and accuracy by applying our method. Experimental results show the better (competitive at least) performance against a state-of-the-art method, while being capable of processing a huge amount of measurements in building large-scale maps
without
blocking the high-accuracy real-time localization outputs.