Collaborative Mapping with Drone Swarms Utilizing Relative Distance Measurements

Sammanfattning: The field of use for unmanned aerial vehicles, UAVs, has completely exploded in the last decade. Today they are used for surveillance missions and inspecting places that are difficult for people to access. To increase the efficiency and robustness in the execution of these types of missions, swarms of cooperating drones can be used. However, that places new demands on which solutions are used for positioning and navigating the agents. This thesis investigates, implements, and evaluates solutions for relative positioning and mapping with drone swarms. Systems for estimating relative poses by fusing velocity data and pairwise distance measurements between agents using an extended Kalman filter (EKF) are investigated and presented in the report. A filter that builds upon an existing approach to estimate relative poses is developed, modified to include all pairwise distances available in the constellation, leading to up to 47 percent more accurate positioning. A multi-dimensional scaling (MDS) initialization procedure is also developed, capable of determining, with good accuracy, the initial relative poses within a swarm, assisting nearly instant convergence for the EKF. Furthermore, another EKF, using MDS coordinate estimates as input, is developed and tested. The drones are equipped with range detectors that measure the distances to the walls in four directions. The distance data is inserted into a grid, discretizing the environment. A method to account for the uncertainty in UAV position when mapping the environment is implemented, leading to improved results. Two ways for a swarm to create a map are tested and shown to be applicable in different setups. If the drones in the swarm have a common coordinate system, the drones update the same grid and create a map. If the coordinate systems of the drones differ, the maps are created individually and merged instead. Generally, the method for collaboratively constructing a map performs better and does not require complex solutions for map merging. To merge the maps, a cost function is needed that measures how well the maps match. Three different cost functions are compared and evaluated. The mapper is evaluated for a swarm exploring the environment using both known global positions and relative pose estimates. The precision achieved with the pre-existing positioning filter is proven to be sufficiently high to generate maps with decimeter resolution when feeding relative pose estimates to the mapping system. A higher mapping resolution is possible in the simulation environment, but requires much more computation time, and was therefore not tested.