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

Abstract

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.

Risks of Drone Use in Light of Literature Studies

“…While [ 152 ] describes the technological or regulatory challenges for UAVs, in [ 204 , 222 ], attention is drawn to the legal-political, socio-legal approach and the implementation of regulations, highlighting the measures that should be taken before making a given technology available to the public. Some of the important areas are planning paths, avoiding obstacles, and navigation [ 143 , 154 , 156 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 ]. The aspects mentioned above have a significant impact on the operational safety of the drone.…”

Design and Verification of Observability-Driven Autonomous Vehicle Exploration Using LiDAR SLAM

“…The exploration method presented in this paper applies the observability analysis technique to the exploration point and path generation process while securing the performance of SLAM. For path generation, the technique of [11] is used.…”

“…As shown in the figure, we prepared three types of The exploration point generation algorithm is performed when the vehicle arrives at a previous exploration point. The exploration target locations generated by this algorithm are passed as arrival points to the path planning technique in [11] to drive the path while ensuring navigation performance. This operation is repeated to complete the exploration.…”