URDF modeling, SLAM techniques, AMCL, Gazebo simulation, and RViz2 visualization. The central objective is to enable

robots to autonomously perceive their surroundings, construct accurate maps, self-localize, and navigate. The integration of

URDF defines robot attributes, SLAM produces precise maps, and AMCL ensures reliable localization. Gazebo facilitates testing,

while RViz2 provides real-time visualization. The outcome is an efficient navigation system empowering robots to independently

navigate intricate environments. Beyond warehousing, applications span service robotics, exploration, and environmental

monitoring. The research's significance lies in addressing a fundamental robotics challenge, advancing autonomous mobility

across sectors. The approach's efficacy is validated through testing, with potential contributions to robotics research and r eal-

world applications. achieved 97% mapping accuracy with a 5% deviation in simulated environments.

Keywords: SLAM, Gazebo, AMCL, Localization, Mapping, Visualization, ROS2

I. Introduction

mobile robots. These robots, known for their maneuverability and versatility, hold promise in domains like logistics, surveillance,

and exploration. To harness their full potential, integration of key technologies becomes paramount.

Problem Statement

A critical challenge lies in creating reliable autonomous navigation systems for differential drive mobile robots. Existing

approaches do not fully integrate ROS2, FastSLAM, and AMCL to enhance localization, mapping accuracy, and navigation

efficiency. Without a cohesive solution, robots risk errors, inefficient path planning, and collisions, impeding their ability to

function optimally in complex and dynamic environments. This shortcoming is particularly impactful given the rising demand for

automation across industries.

The significance of addressing this problem cannot be overstated. Failure to provide a comprehensive solution inhibits the

realization of efficient and safe autonomous navigation, limiting the potential of these robots in diverse applications. The

obstacles.

The research objectives are outlined as follows:

Identify areas within the implemented system that may necessitate further research and refinement to enhance the functionality

and effectiveness of the autonomous navigation system for differential drive mobile robots.

Conduct a comprehensive literature review to identify a suitable SLAM algorithm and localization method for the development of

an autonomous navigation system for differential drive mobile robots, focusing on the integration of Grid-Based FastSLAM,

AMCL, and URDF within ROS2.

Implement the chosen Grid-Based FastSLAM algorithm and AMCL method within the ROS2 framework and the Gazebo

simulator as part of the autonomous navigation system's development.

Evaluate the performance of the integrated Grid-Based FastSLAM and AMCL system in diverse simulated and visualization

In recent years, the field of autonomous robotics has experienced remarkable advancements, revolutionizing various industries

through breakthroughs in navigation, obstacle avoidance, simultaneous localization, and mapping (SLAM), and map generation

techniques. These advancements have empowered robots to operate independently in complex and dynamic environments, with

applications spanning forestry, healthcare, agriculture, construction, and general robotics. This literature review aims to explore

the key studies and research conducted in these domains, with a specific focus on the progress made in autonomous navigation

and the development of comprehensive navigation systems for mobile robots.

Within the field of autonomous navigation, a multitude of studies have significantly contributed to its advancement. This review

will delve into the exploration of innovative sensor technologies employed to accurately perceive the environment. These sensors,

which encompass lidar, cameras, and inertial measurement units (IMUs), serve as critical sources of information for robots,

enhancing mapping capabilities have emerged. (Achour et al., 2022) conducted a comprehensive survey focusing on semantic

mapping in the context of both single mobile robots in indoor environments and collaborative mobile semantic mapping. Their

survey encompassed a review of contemporary solutions for acquiring semantic data, including techniques such as object

detection/recognition, deep-learning-based segmentation, and innovative human-robot interaction approaches. Additionally, the

authors delved into reasoning-based acquisition methods, emphasizing the capability of robots to infer new information based on

acquired data and a knowledge database. While highlighting the substantial progress made in semantic mapping, this survey also

shed light on the challenges that persist, such as semantic data gathering, map representation, environmental considerations, and

the complexities of collaboration among mobile robots.

In parallel to the investigation into semantic mapping, (Qu et al., 2021) contributed to the mapping literature by conducting a

comparative analysis of three 2D Simultaneous Localization and Mapping (SLAM) algorithms: Gmapping, Hector SLAM, and

Building upon this exploration of SLAM algorithms, (Yagfarov et al., 2018) conducted a study comparing three popular ROS-

based 2D SLAM libraries: Google Cartographer, Gmapping, and Hector SLAM. Their evaluation employed precise ground truth

data to assess map accuracy. The results consistently favored Google Cartographer as the top performer in producing highly

accurate maps, followed by Gmapping. In contrast, Hector SLAM, relying solely on LIDAR data and lacking loop closure

mechanisms, yielded less accurate results. The implications of this research were clear, suggesting Google Cartographer as a

preferred choice for generating high-quality 2D maps with LIDAR on mobile robots.

Extending beyond the realm of robotics but still relevant to mapping, (Cho, Lee, & Kim 2023) introduced a novel approach to

autonomous vehicle motion planning using occupancy grid maps. Their method incorporated a single constraint based on the

occupancy grid map, permitting traversal only in cells with values below a predefined threshold. This approach simplified

complex motion planning problems and reduced computational costs by integrating the constraint into the motion planning

Localization:

The field of mobile robotics has seen substantial advancements in the area of localization. Researchers have been hard at work,

aiming to boost precision, resilience, and adaptability in this crucial aspect of robotics. Among the various localization methods

explored, the Adaptive Monte Carlo Localization (AMCL) algorithm stands out as a noteworthy approach. (Baek et al., 2022)

contributed to the domain by developing a 3D global localization method tailored specifically for underground mines. They

harnessed mobile LiDAR mapping and point cloud registration techniques to achieve precise matching of local point cloud

datasets. This innovation holds tremendous promise for enhancing 3D position recognition in the challenging and dynamic

environments of underground mines.

For mobile robots, maintaining global pose awareness is crucial, especially when faced with scenarios such as kidnap recovery.

(Li et al., 2023) proposed an ingenious solution based on ultra-wide-band technology. Their adaptive Monte Carlo localization

Efficient localization techniques play a pivotal role in logistics, particularly in the warehousing domain. (Tripicchio et al., 2022)

conducted a comprehensive study comparing different methods for 3D multilateration of passive UHF RFID tags. Their research

demonstrated centimeter-level accuracy for 3D localization and sub-centimeter accuracy for 2D localization, offering valuable

insights into optimizing localization techniques in warehouse logistics.

In the context of AMCL, (Wang et al., 2018) addressed critical challenges such as initial location speed, stability, and robustness.

Their proposed algorithm, grounded in a UWB array, aimed to enhance the performance of the Adaptive Monte Carlo

Localization algorithm. This advancement holds great promise for improving the localization of mobile robots.

(Singh et al., 2023) introduced a robust methodology for implementing localization and navigation schemes in autonomous

mobile robots. By harnessing the Robot Operating System (ROS) framework and integrating various ROS packages and

algorithms, their approach achieved highly accurate pose identification, mapping, and robot control, both in simulations and real-

information fusion SLAM method, based on Runge-Kutta improved pre-integration, significantly improved accuracy by avoiding

errors caused by first-order approximation.

In the construction industry, (Xiang et al., 2022) addressed the challenge of SLAM for construction machinery in dynamic

accuracy and optimize path planning in complex environments. By integrating data from multiple sensors and employing fuzzy

control algorithms, this approach promises improved obstacle avoidance, laying a foundation for future research in this area. (Li

avoidance of dynamic obstacles. This approach exhibits potential in enhancing local obstacle avoidance performance, benefiting

the operability of mobile robots in dynamic environments. (Li et al., 2023) explored an intriguing concept of intelligent physical