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This paper presents a Light Detection and Ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM) system “SpyBot,” integrated with ultrasonic sensors for safety, security, and environmental welfare applications. The integration of LiDAR enables precise mapping and autonomous navigation, while ultrasonic sensors enhance obstacle detection and avoidance. Previous surveillance robots relied on complex, costly processing units, making them bulky and expensive. In contrast, SpyBot utilizes lightweight gear motors along with an Arduino and Raspberry Pi, integrated with open-source tools. This reduces the overall cost. The system uses Robot Operating System (ROS) 2 Jazzy as the core framework, ensuring efficient communication between hardware and software components. To validate the robot’s performance, simulations were conducted in Gazebo Sim (GZ Sim), allowing for testing in a controlled environment before deployment. Additionally, RViz2 was utilized for real-time visualization of LiDAR data and mapping outputs. This comprehensive approach ensures reliable navigation and adaptability in diverse operational scenarios.

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SpyBot: A Lightweight Robot that Works on LiDAR SLAM

  • 会议论文

  • 发表日期：

    2026年

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  • Venkatesh Patel,
  • Geeta Rani

摘要

This paper presents a Light Detection and Ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM) system “SpyBot,” integrated with ultrasonic sensors for safety, security, and environmental welfare applications. The integration of LiDAR enables precise mapping and autonomous navigation, while ultrasonic sensors enhance obstacle detection and avoidance. Previous surveillance robots relied on complex, costly processing units, making them bulky and expensive. In contrast, SpyBot utilizes lightweight gear motors along with an Arduino and Raspberry Pi, integrated with open-source tools. This reduces the overall cost. The system uses Robot Operating System (ROS) 2 Jazzy as the core framework, ensuring efficient communication between hardware and software components. To validate the robot’s performance, simulations were conducted in Gazebo Sim (GZ Sim), allowing for testing in a controlled environment before deployment. Additionally, RViz2 was utilized for real-time visualization of LiDAR data and mapping outputs. This comprehensive approach ensures reliable navigation and adaptability in diverse operational scenarios.