Markov Parallel Tracking and Mapping for Probabilistic SLAM

Abstract

Parallel tracking and mapping (PTAM) as a time-efficient framework for simultaneous localization and mapping (SLAM) has been becoming popular in recent years. However, in this paper, we vigilantly point out that the favorite parallel-pipeline design realized by recent proposed SLAM algorithms may lead to inaccurate state estimates which, as a consequence, cannot always guarantee the performance of the estimators in real application. This is mainly due to the imperfect design for processing loop-closure measurements which accidentally violates the Markov assumption for probabilistic SLAM problem. To address this issue, a novel estimator design is proposed that holds the advantage of parallel processing, while striving to be consistent with the Markov property of the batch probabilistic SLAM estimator, therefore, termed Markov parallel tracking and mapping (MPTAM). Especially, the experiments on challenging visual-inertial datasets are employed to further demonstrate the improvements of proposed estimator in terms of accuracy and efficiency, as compared with the state-of-the-art SLAM system.