Fast Structural Representation and Structure-aware Loop Closing for Visual SLAM
Shuxiang Xie, Ryoichi Ishikawa, Ken Sakurada, Masaki Onishi, Takeshi Oishi
Abstract
Perceptual Aliasing is one of the main problems in simultaneous localization and mapping (SLAM). Wrong associations between different places may lead to failure of the whole map. Research on structure information is rarely investigated among existing solutions to this problem. In cases of visual SLAM without sensors, such as LiDAR or Inertial Measurement Unit (IMU), structure information can rarely be obtained due to the sparsity of 3D points, which also makes structure analysis complex. This study provides a spherical harmonics (SH) based fast structural representation (SH-FS) in visual SLAM using sparse point clouds, which extracts the structure information from sparse points into single vector. SH-FS was applied in conventional feature-based loop closing process. Furthermore, a structure-aware loop closing method in visual SLAM was proposed to improve the robustness of SLAM systems. Moreover, our methods show a favorable performance in extensive experiments on different large-scale real world datasets.
BibTeX
@inproceedings{iros2022_faststructuralre,
title = {Fast Structural Representation and Structure-aware Loop Closing for Visual SLAM},
author = {Shuxiang Xie and Ryoichi Ishikawa and Ken Sakurada and Masaki Onishi and Takeshi Oishi},
booktitle = {IROS 2022},
year = {2022}
}