Outlier Elimination for Monocular Object SLAM Based on Spatiotemporal Consistency Constraints

Abstract

Object-level simultaneous localization and mapping (SLAM) is critical for mobile robot localization and navigation. Wrong observations due to monocular camera noise and object detection errors affect accurate object perception. Most of the existing work adopts simple artificial rules to prevent the construction of object outliers. These strategies are difficult to universalize to different challenging scenarios. Eliminating object outliers remains a challenge for object SLAM. In this article, we propose a spatiotemporal consistency model for removing object outliers. Our approach takes only a low-cost monocular camera as the image sensor of the system. We use the graph model to construct spatial consistency as a means to constrain the semantic spatial relationships among multiple objects. Only the objects that satisfy the spatial consistency constraints are constructed. In addition, outliers are detected based on the regularity of object measurements that appear on the time axis. We eliminate the objects with observations in consecutive frames that do not satisfy the temporal consistency constraint. Finally, we couple normal objects to SLAM for pose optimization to improve camera localization accuracy. Experiments on public datasets and a real scenario demonstrate the performance of the proposed approach.