Multi-Objective Optimization of RTAB-Map parameters using Genetic Algorithm for indoor 2D SLAM

Abstract

Currently, Robot Operating System (ROS) provides multiple packages to implement different Simultaneous Localization and Mapping (SLAM) approaches. To effectively obtain sensor data, these packages use parameters whose values are set from prior knowledge and experience with robots and SLAM. In this paper, using a Multi-Objective Genetic Algorithm (MOGA) to optimize the values for these parameters is proposed. MOGA allows trade-offs between the objectives using Pareto dominance techniques. Three parameters from the RTAB-Map package are considered for optimization using three different MOGA mechanisms, Dominance Count, Dominance Rank and Switching Fitness. The quality of the map generated for every set of parameters is taken as the indicator of its performance. The number of corners, number of contours and the proportion of occupied cells in the map are used as quantitative measures of map quality. Finally, results obtained from testing the algorithm in simulation are used to test a Quanser QBot2 robot.