ABSTRACT:
This paper presents the design and real-world implementation of an indoor mobile robot capable of autonomous navigation using ROS, a Raspberry Pi 3 controller, and a TF Mini LiDAR sensor embedded on two servo motors (tilt and pan). The robot employs Simultaneous Localization and Mapping (SLAM) to build a 2D map of an unknown indoor environment while localizing itself within that map. A global planner (Rapidly-Exploring Random Tree, RRT) and local trajectory controller compute collision-free paths to user-defined goals. Ultrasonic rangefinders and the LiDAR provide real- time obstacle detection for reactive avoidance. We validated the system in a laboratory setting: the robot successfully mapped the area and autonomously navigated between waypoints without collisions. Our results demonstrate the effectiveness of low-cost sensors and open-source ROS software for learning foundational concepts of indoor robotics (localization, mapping, planning, and control). This work may aid educational and service applications in homes, hospitals, offices, etc., where autonomous mobile robots reduce manual labor (e.g., delivery of items, surveillance). The approach integrates sensor data processing, filter-based SLAM, and reactive path planning. Our results demonstrate effective localization and navigation performance consistent with recent literature.
Cite this article:
Ram Ashish Maurya, Riya Tiwari, Aayush Vikram Singh (2025), Smart Autonomous Indoor Navigation Robot Using ROS and LiDAR-Based SLAM. Spectrum of Emerging Sciences, 5 (2) 24-30, 10.55878/SES2025-5-2-7, DOI: https://doi.org/10.55878/SES2025-5-2-7
Reference:
1.
Adán
A, Quintana B, Vázquez AS, Olivares A, Parra E, Prieto S. Towards the automatic
scanning of indoors with robots. Sensors (Basel). 2015;15(5):11551–11574.
2.
Bajpai
A, Amir-Mohammadian S. Towards an indoor navigation system using monocular
visual SLAM. In: Proc
IEEE 45th Annu Comput Softw Appl Conf (COMPSAC); 2021. p. 520–525.
3.
Chan
TH, Hesse H, Ho SG. Lidar-based 3D SLAM for indoor mapping. In: Proc 7th Int Conf Control Autom Robot
(ICCAR); 2021. p.
285–289.
4.
DeSouza
GN, Kak AC. Vision for mobile robot navigation: A survey. IEEE Trans Pattern Anal Mach Intell. 2002;24(2):237–267.
5.
Fan Z,
Zhang L, Wang X, Shen Y, Deng F. Lidar, IMU, and camera fusion for simultaneous
localization and mapping: A systematic review. Artif Intell Rev. 2025;58(6):1–59.
6.
Huang
J, Junginger S, Liu H, Thurow K. Indoor positioning systems of mobile robots: A
review. Robotics. 2023;12(2):47.
7.
Karur
K, Sharma N, Dharmatti C, Siegel JE. A survey of path planning algorithms for
mobile robots. Vehicles. 2021;3(3):448–468.
8.
Katona
K, Neamah HA, Korondi P. Obstacle avoidance and path planning methods for
autonomous navigation of mobile robot. Sensors (Basel). 2024;24(11):3573.
9.
Liu Y,
Wang S, Xie Y, Xiong T, Wu M. A review of sensing technologies for indoor
autonomous mobile robots. Sensors
(Basel).
2024;24(4):1222.
10.
Maaref
H, Barret C. Sensor-based fuzzy navigation of an autonomous mobile robot in an
indoor environment. Control
Eng Pract.
2000;8(7):757–768.
11.
Megalingam
RK, Teja CR, Sreekanth S, Raj A. ROS-based autonomous indoor navigation
simulation using SLAM algorithm. Int J Pure Appl Math. 2018;118(7):199–205.
12.
Noh S,
Park J, Park J. Autonomous mobile robot navigation in indoor environments:
Mapping, localization, and planning. In: Proc Int Conf Inf Commun Technol Convergence (ICTC); 2020. p. 908–913.
13.
Raspberry
Pi Foundation. Raspberry Pi: What is a Raspberry Pi? 2017.
14.
Rodríguez-Martínez
EA, Flores-Fuentes W, Achakir F, Sergiyenko O, Murrieta-Rico FN. Vision-based
navigation and perception for autonomous robots: Sensors, SLAM, control
strategies, and cross-domain applications—a review. Eng. 2025;6(7):153.
15.
Sánchez-Ibáñez
JR, Pérez-del Pulgar CJ, García-Cerezo A. Path planning for autonomous mobile
robots: A review. Sensors
(Basel).
2021;21(23):7898.
16.
Siegwart
R, Nourbakhsh IR, Scaramuzza D. Introduction to autonomous mobile robots. Cambridge (MA): MIT Press; 2011.
17.
Tran
TQ, Becker A, Grzechca D. Environment mapping using sensor fusion of 2D laser
scanner and 3D ultrasonic sensor for a real mobile robot. Sensors (Basel). 2021;21(9):3184.
18.
Ugwoke
KC, Nnanna NA, Abdullahi SEY. Simulation-based review of classical, heuristic,
and metaheuristic path planning algorithms. Sci Rep.
2025;15(1):12643.