A robust autonomous navigation and mapping system based on GPS and LiDAR data for unconstraint environment

For autonomous navigation, three main functions are essential: finding the location, creating the mapping, and getting the optimum path. Since Human operators study the map and correlate it to aerial pictures to locate target locations, consistent localization and mapping concurrently are challenging tasks. Light detection and ranging (LiDAR) can create a 2-dimensional (2D) map and generate positional data for the indoor area, but it fails in the presence of a dynamic object. The global positioning systems (GPS) data offering precise location tracking in outdoor spaces can tackle the weakness of LiDAR. Therefore, we design a robot operating system (ROS) based vehicular system integrating GPS and LiDAR data. The Inertial Measurement Unit (IMU) is used to make an educated approximation for LiDAR registration. A Rao-Blackwellized particle filter (RBPF) based Gmapping algorithm has been retreated in the proposed system using sensors data for navigation and mapping, where each particle has its map of the surrounding. The computational complexity due to large particle formation in RBPF is solved using Gaussian distribution based convergence. The experiments are carried out in moderate room size and large size room environments with obstacles and without obstacles. It generates a 2D map of unknown environments minimizing the cumulative error due to relative measurement of LiDAR data. The proposed system provides autonomous driving in an unfamiliar environment increasing localization accuracy by solving the error accumulation problem in an unconstraint environment. The Gmapping based proposed implementation succeeded to generate maps accurately with a trajectory error of about 0.094 cm.