Incremental structural modeling on sparse visual SLAM

Rafael Alves Roberto, Hideaki Uchiyama, João Paulo Silva do Monte Lima, Hajime Nagahara, Rin-Ichiro Taniguchi, Veronica Teichrieb

Abstract: This paper presents an incremental structural modeling approach that improves the precision and the stability of existing batch-based ones for sparse and noisy point clouds from visual simultaneous localization and mapping (SLAM). The main idea is to use the generating process of point clouds on SLAM effectively. First, a batch-based method is applied to point clouds that are incrementally generated from SLAM. Then, the temporal history of reconstructed geometric primitives is statistically analyzed to suppress incorrect reconstruction. The evaluation shows that both precision and stability are improved compared to an existing batch-based method, and the proposed method is suitable for real-time structural modeling.