Observability-Driven Path Planning Design for Securing Three-Dimensional Navigation Performance of LiDAR SLAM

This paper presents an efficient method for securing navigation performance by suppressing divergence risk of LiDAR SLAM through a newly proposed geometric observability analysis in a three-dimensional point cloud map. For this, observability characteristics are introduced that quantitatively evaluate the quality of the geometric distribution of the features. To be specific, this study adapts a 3D geometric observability matrix and the associated condition number for developing numerical benefit. In an extensive application, we implemented path planning in which the enhanced SLAM performs smoothly based on the proposed method. Finally, to validate the performance of the proposed algorithm, a simulation study was performed using the high-fidelity Gazebo simulator, where the path planning strategy of a drone depending on navigation quality is demonstrated. Additionally, an indoor autonomous vehicle experimental result is presented to support the effectiveness of the proposed algorithm.