Full State History Cooperative Localisation with Complete Information Sharing

This thesis presents a decentralised localisation method for multiple robots. We enable reduced bandwidth requirements whilst using local solutions that fuse information from other robots. This method does not specify a communication topology or require complex tracking of information. The methods for including shared data match standard elements of nonlinear optimisation algorithms. There are four contributions in this thesis. The first is a method to split the multiple vehicle problem into sections that can be iteratively transmitted in packets with bandwidth bounds. This is done through delayed elimination of external states, which are states involved in intervehicle observations. Observations are placed in subgraphs that accumulate between external states. Internal states, which are all states not involved in intervehicle observations, can then be eliminated from each subgraph and the joint probability of the start and end states is shared between vehicles and combined to yield the solution to the entire graph. The second contribution is usage of variable reordering within these packets to enable handling of delayed observations that target an existing state such as with visual loop closures. We identify the calculations required to give the conditional probability of the delayed historical state on the existing external states before and after. This reduces the recalculation to updating the factorisation of a single subgraph and is independent of the time since the observation was made. The third contribution is a method and conditions for insertion of states into existing packets that does not invalidate previously transmitted data. We derive the conditions that enable this method and our fourth contribution is two motion models that conform to the conditions. Together this permits handling of the general out of sequence case

Bounding Drift in Cooperative Localisation Through the Sharing of Local Loop Closures

Handling loop closures and intervehicle observations in cooperative robotic scenarios remains a challenging problem due to data consistency, bandwidth limitations and increased computation requirements. This paper develops a general cooperative localisation and single vehicle Visual SLAM framework that includes direct intervehicle observations and pose to pose loop closures on each vehicle with states shared as required. This fuses single vehicle SLAM with cooperative localisation and avoids data association of map data across limited communication networks. The base problem is developed as a factor graph with each vehicle solving local subgraphs that are split based on intervehicle observations. We modify the order of variable elimination in subgraphs through manipulation of the square-root of the Information matrix to extract updates that include the historic states involved in the loop closures and do not require transmission of other states not involved in the measurement or retransmission of previously shared states. We demonstrate the effect on localisation accuracy and bandwidth using data captured from a set of five robots observing each other and landmarks compared to both single vehicle SLAM, pure cooperative localisation and a centralised solution