Abstract
Visual SLAM (abbreviates ‘simultaneous localization and mapping’) is a promising solution for environment mapping. This study is devoted to a description of a semantically ensembled SLAM framework. For structural indoor scenes, the structured lines and planes can serve as the newly added constraints to improve the positioning accuracy. In this paper, we propose to comprehensively incorporate point-line-plane primitives and construct a tightly coupled camera pose estimator without any environment assumptions. In particular, the maximum number of extracted lines features is numerically determined. We further integrate a lightweight object mapping pipeline with the designed pose estimator. In this pipeline, the leveraging of fitted plane and cuboid landmarks enables an online, CPU-based dense mapping. The tests on ICL-NUIM and TUM benchmark datasets illustrate that, in comparison to ORB-SLAM2, PL-SLAM (Point and Line based SLAM), SP-SLAM (Supposed Plane SLAM) and PLP-SLAM (Point, Line and Plane fused SLAM), our design leads to superior performances in global consistency and system drift elimination. The feature detection and multi-level map reconstruction results are simultaneously provided.
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