Decentralized cooperative trajectory estimation for autonomous underwater vehicles

Abstract

Abstract-Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability of of the acoustic channel used to communicate when submerged. Here we propose a CL algorithm specifically designed for full trajectory, or maximum a posteriori, estimation for AUVs. The method is exact and has the advantage that the broadcast packet sizes increase only linearly with the number of AUVs in the collective and do not grow at all in the case of packet loss. The approach allows for AUV missions to be achieved more efficiently since: 1) vehicles waste less time surfacing for GPS fixes, and 2) payload data is more accurately localized through the smoothing approach.

Communication-constrained multi-AUV cooperative SLAM

“…This homogeneous topology has been used previously for CL [16]. In our recent work [17], we improve the scalability of [16] to allow larger team sizes and better robustness to failed communications.…”

“…To overcome the fact that broadcast packet reception is unknown until an acknowledgment is successfully received, a system of bookkeeping using confirmed ingoing and outgoing contact points is used [17]. Bookkeeping is required for vehicles to know which local factors should be generated to guarantee consistency of the multi-vehicle estimates maintained by others.…”

Cooperative Localization for Mobile Agents: A Recursive Decentralized Algorithm Based on Kalman-Filter Decoupling

“…The dominant trend in developing decentralized cooperative localization algorithms in this way is to distribute the computation of components of a centralized algorithm among team members. Some of the examples for this class of D-CL is given in [31], [22], [14], [32], [33]. In a straightforward fashion, decentralization can be conducted as a multi-centralized CL, wherein each agent broadcasts its own information to the entire team.…”

“…Subsequently, [14], [33] present D-CL strategies using maximum-a-posteriori (MAP) estimation procedure. In the former, computations of a centralized MAP is distributed among all the team members.…”

Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

“…19 Other works have investigated the use of multiple cooperating vehicles which share varying amounts of information to further constrain their navigation. [20][21][22] A significant challenge in using inter-vehicle ranges for position estimates is preventing overconfidence in the solution through the sharing of correlated covariance information or double counting. 23 Prior work has largely dealt with this challenge by preserving prior measurement information and extending the state with each new measurement which has limited scalability and suitability for real-time processing.…”