Propagation of Uncertainty in Cooperative Multirobot Localization: Analysis and Experimental Results

Abstract

Abstract. This paper examines the problem of cooperative localization for the case of large groups of mobile robots. A Kalman filter estimator is implemented and tested for this purpose. The focus of this paper is to examine the effect on localization accuracy of the number N of participating robots and the accuracy of the sensors employed. More specifically, we investigate the improvement in localization accuracy per additional robot as the size of the team increases. Furthermore, we provide an analytical expression for the upper bound on the positioning uncertainty increase rate for a team of N robots as a function of N , the odometric and orientation uncertainty for the robots, and the accuracy of a robot tracker measuring relative positions between pairs of robots. The analytical results derived in this paper are validated both in simulation and experimentally for different test cases.

Observability-based consistent EKF estimators for multi-robot cooperative localization

“…To date, theoretical studies on the properties of CL have focused on issues such as initialization [16][17][18][19], system observability [20,16,18], accuracy bounds [21,22], and the complexity of deterministic (static) robot network localization [23]. However, to the best of our knowledge, prior to [15], no work has analytically examined the consistency of CL.…”

“…Based on the assumption of a complete RMG, two measurements, z (12) k o +ℓ and z (21) k o +ℓ , are available at time-step k o + ℓ. Thus, the measurement JacobianH k o +ℓ in this case can be written as (see (12)- (14))…”

Decentralized cooperative trajectory estimation for autonomous underwater vehicles

“…The rate of uncertainty growth decreases as the size of the robot team increases, but is subject to the law of diminishing returns [7]. In addition, the rate of uncertainty growth is independent of the accuracy or frequency of the inter-vehicle measurements [8].…”

The Hybrid Reciprocal Velocity Obstacle

“…As a next step, we would like to equip each robot with purely localized sensing and computing, as in [30], which uses odometry, orientation sensors, and relative positions to estimate global positions. Our approach can be applied without adaptation if data is gathered locally, and the hybrid reciprocal velocity obstacles are defined just as well using only the relative positions and velocities of the robots.…”