Monocular Visual-Inertial Odometry with Planar Regularities

State-of-the-art monocular visual-inertial odometry (VIO) approaches rely on sparse point features in part due to their efficiency, robustness, and prevalence, while ignoring high-level structural regularities such as planes that are common to man-made environments and can be exploited to further constrain motion. Generally, planes can be observed by a camera for significant periods of time due to their large spatial presence and thus, are amenable for long-term navigation. Therefore, in this paper, we design a novel real-time monocular VIO system that is fully regularized by planar features within a lightweight multi-state constraint Kalman filter (MSCKF). At the core of our method is an efficient robust monocular-based plane detection algorithm, which does not require additional sensing modalities such as a stereo or depth camera as commonly seen in the literature, while enabling real-time regularization of point features to environmental planes. Specifically, in the proposed MSCKF, long-lived planes are maintained in the state vector, while shorter ones are marginalized after use for efficiency. Planar regularities are applied to both in-state SLAM features and out-of-state MSCKF features, thus fully exploiting the environmental plane information to improve VIO performance. The proposed approach is evaluated with extensive Monte-Carlo simulations and different real-world experiments including an author-collected AR scenario, and shown to outperform the point-based VIO in structured environments. Video Demonstration https://youtu.be/bec7LbYaOS8 AR Table Dataset https://github.com/rpng/ar_table_dataset