Environment Model Generation And Localisation Of Mobile Indoor Autonomous Robots

Autonomous Mobile Robots (AMR) are gaining traction owing to their ability to perform complicated tasks that require navigation in complex and dynamic indoor environments, thus, leading to the replacement of manual workforce with an efficient and affordable robotic system with greater precision, accuracy and minimal error. This paper focuses on developing a system which is based on the two important aspects that determine the performance of an indoor AMR i.e. environment model generation and localisation of an indoor AMR. The perception system is based on the representation and processing of the data obtained from proprioceptive sensors. So far, the Bayesian Occupancy Grid (OG) mapping is the best approach for environment model generation in mobile robotics. The grid mapping approach is used owing to its higher efficiency, better accuracy, faster implementation and probabilistic framework. Localisation is complicated in indoor environments such as warehouses as GPS is not reliable. This is achieved using Hector Simultaneous Localisation And Mapping (SLAM) and Adaptive Monte Carlo Localisation (AMCL) techniques using data received from a 2D-Light Detection And Ranging (LiDAR). Robot Operating System (ROS) is used as the core to design the mobile robot system with high performance and scalability. The simulation environment and robot are created in Gazebo, and visualised using Rviz. The generated OG and localisation results are compared with the ground truth, and its performance analysis is done.