Over the past five decades, Autonomous Mobile Robots (AMRs) have been an active research field. Maps of high accuracy are required for AMRs to operate successfully. In addition to this, AMRs needs to localise themselves reliably relative to the map. Simultaneous Localisation and Mapping (SLAM) address the problem of both map building and robot localisation. When exploring large areas, Multi-Robot SLAM (MRSLAM) has the potential to be far more efficient and robust, while sharing the computational burden across robots. However, MRSLAM encounters issues such as difficulty in map fusion of multi-resolution maps, and unknown relative positions of the robots. This thesis describes a distributed multi-resolution map merging algorithm for MRSLAM. HectorSLAM, which is one of many single robot SLAM implementations, has demonstrated exceptional results and was selected as the basis for the MRSLAM implementation in this project. We consider the environment to be three-dimensional with the maps being constrained to a two-dimensional plane. Each robot is equipped with a laser range sensor for perception and has no information regarding the relative positioning of the other robots. The experiments were conducted both in simulation and a real-world environment. Up-to three robots were placed in the same environment with Hector-SLAM running, the local maps and localisation were then sent to a central node, which attempted to find map overlaps and merge the resulting maps. When evaluating the success of the map merging algorithm, the quality of the map from each robot was interrogated. Experiments conducted on up to three AMRs show the effectiveness of the proposed algorithms in an indoor environment.

Multi-robot systems are an established research area with a growing number of applications. Efficient coordination in such systems usually requires knowledge of robot positions and the global map. This work presents a novel map-merging algorithm for merging 3D point cloud maps in multi-robot systems, which produces the global map and estimates robot positions. The algorithm is based on feature- matching transformation estimation with a novel descriptor matching scheme and works solely on point cloud maps without any additional auxiliary information. The algorithm can work with different SLAM approaches and sensor types and it is applicable in heterogeneous multi-robot systems. The map-merging algorithm has been evaluated on real-world datasets captured by both aerial and ground-based robots with a variety of stereo rig cameras and active RGB-D cameras. It has been evaluated in both indoor and outdoor environments. The proposed algorithm was implemented as a ROS package and it is currently distributed in the ROS distribution. To the best of my knowledge, it is the first ROS package for map-merging of 3D maps.

This dissertation presents a novel multi-robot coordination control algorithm for search and rescue (SAR) operations. Continuous and rapid coverage of the unstructured and complex disaster areas in search of possible buried survivors is a time critical operation where prior information about the environment is either not available or very limited. Human navigation of such areas is definitely dangerous due to the nature of the debris. Hence, exploration of unknown disaster environments with a team of robots is gaining importance day by day to increase the efficiency of SAR operations. Localization of possible survivors necessitates uninterrupted navigation of robotic aiding devices within the rubbles without getting trapped into dead ends. In this work, a novel goal oriented prioritized exploration and map merging methodologies are proposed to generate efficient multi-robot coordination control strategy. These two methodologies are merged to make the proposed methodology more realistic for real world applications. Prioritized exploration of an environment is the first important task of the efficient coordination control algorithm for multi-robots. A goal oriented and prioritized exploration approach based on a percolation model for victim search operation in unknown environments is presented in this work. The percolation model is used to describe the behavior of liquid in random media. In our approach robots start prioritized exploration beginning from regions of the highest likelihood of finding victims using percolation model inspired controller. A novel map merging algorithm is presented to increase the performance of the SAR operation in the sense of time and energy. The problem of merging partial occupancy grid environment maps which are extracted independently by individual robot units during search and rescue (SAR) operations is solved for complex disaster environments. Moreover, these maps are combined using intensity and area based features without knowing the initial position and orientation of the robots. The proposed approach handles the limitation of existing works in the literature such as / limited overlapped area between partial maps of robots is sufficient for good merging performance and unstructured partial environment maps can be merged efficiently. These abilities allow multi-robot teams to efficiently generate the occupancy grid map of catastrophe areas and localize buried victim in the debris efficiently.