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This paper reports on an underwater cooperative localization algorithm for faulty low-bandwidth communication channels based on a factor graph estimation framework. Vehicles measure the one-way-travel-time (OWTT) of acoustic broadcasts to obtain a relative range observation to the transmitting vehicle. We present a method to robustly share locally observed sensor data across the network by exploiting odometry factor composition. Our algorithm calls on approximate marginalization techniques to compute a compact set of informative factors that enable local navigation data to be shared efficiently. We provide results from a realtime implementation of our algorithm using two autonomous underwater vehicles and a surface vehicle.

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State-of-the-art robotic perception systems have achieved sufficiently good performance using Inertial Measurement Units (IMUs), cameras, and nonlinear optimization techniques, that they are now being deployed as technologies. However, many of these methods rely significantly on vision and often fail when visual tracking is lost due to lighting or scarcity of features. This paper presents a state-estimation technique for legged robots that takes into account the robot's kinematic model as well as its contact with the environment. We introduce forward kinematic factors and preintegrated contact factors into a factor graph framework that can be incrementally solved in real-time. The forward kinematic factor relates the robot's base pose to a contact frame through noisy encoder measurements. The preintegrated contact factor provides odometry measurements of this contact frame while accounting for possible foot slippage. Together, the two developed factors constrain the graph optimization problem allowing the robot's trajectory to be estimated. The paper evaluates the method using simulated and real sensory IMU and kinematic data from experiments with a Cassie-series robot designed by Agility Robotics. These preliminary experiments show that using the proposed method in addition to IMU decreases drift and improves localization accuracy, suggesting that its use can enable successful recovery from a loss of visual tracking.Exp(φ)R = RExp(R T φ).(3)