STL-SLAM: A Structured-Constrained RGB-D SLAM Approach to Texture-Limited Environments

Most RGB-D-based SLAM methods assume texture-rich environments, making them susceptible to significant tracking errors or complete failures in the absence of texture features. Moreover, many existing methods encounter substantial rotation estimation errors, leading to long-term drift in tracking. This paper proposes a novel structured-constrained RGB-D SLAM method (STL-SLAM) for texture-limited environments. Compared to the existing methods, STL-SLAM can deal with environments without abundant texture information and significantly reduce long-term drift caused by rotation estimation errors. We assess the distribution complexity of pixels in an image by calculating the information entropy and pre-processing accordingly. We also present an efficient Manhattan Frames (MF) detection strategy based on orthogonal planes and lines. If MF is detected, we decouple rotation and translation, estimate drift-free rotation based on the Manhattan World (MW) coordinate system, and then estimate translation by minimizing the re-projection error of point, line, and plane features. In non-Manhattan Frames, the 6-DoF pose estimation is performed holistically, with the incorporation of structural constraints of parallel and perpendicular planes, as well as parallel and vertical lines, into the optimization process. Finally, we evaluate our method on public datasets and in real-world environments, which shows that our proposed method achieves superior performance compared to its counterparts.