Literature Review: Gas Concentration Mapping Using UAV with SLAM and Kernel-DM Algorithms.

Abstract

The increasing consumption of natural gas in Indonesia necessitates enhanced safety monitoring systems for gas infrastructure. This paper presents a comprehensive literature review on gas concentration mapping using Unmanned Aerial Vehicles (UAVs) integrated with Simultaneous Localization and Mapping (SLAM) and Kernel Distribution Mapping (Kernel-DM) algorithms. The proposed system addresses the challenges of gas leak detection in extensive distribution areas by leveraging UAV mobility, SLAM-based accurate localization, and Kernel-DM for generating continuous gas concentration maps. This approach offers advantages in terms of coverage area, obstacle avoidance, exploration efficiency, and real-time monitoring capabilities. The integration of Hector SLAM with GPS and LIDAR sensors provides robust localization in GPS-denied or uncertain environments, while Kernel-DM enables statistical modeling of gas distribution patterns. This review synthesizes existing research, identifies gaps in current methodologies, and proposes an enhanced system architecture for real-time gas concentration mapping in unknown environments.