Real-Time Map Generation by VLP-16 LiDAR sensor in GPS denied environment

Navigation and orientation are severely relatable by 3D mapping in GPS-denied situations, especially for real-time applications. The usefulness of a 3D LiDAR, VLP 16 sensor for producing precise, high-resolution maps in an environment is confirmed by this paper investigation. To guarantee adaptability in dynamic and unstructured environments, the recommended strategy incorporates modern SLAM techniques. To verify LiDAR-generated maps for alignment accurateness and real-time performance, they are compared to google image data. According to experimental results, the system can create accurate 3D maps when GPS is unavailable, highlighting its potential for autonomous navigation, search and rescue missions, and urban exploration. The methodology improves the development of dependable autonomous systems in difficult environments by offering a framework for evaluating LiDAR-based mapping systems using defined references. The paper focuses on modifying and utilizing a real-time 3D mapping technique i.e. hdl graph slam that was created using python and C++. The setup of the algorithm and its application to SLAM assessment are described in this paper. The arrangement was implemented utilizing a 16 Channel LiDAR sensor on a mobile robot i.e. the Henes T780. When compared to google image, it is demonstrated that the HDL (high definition LiDAR) graph slam software methods and hardware combination produce accurate and good mapping results.