“…In Wu et al, 27 distributed Bayesian cooperative localization was solved via Gaussian message passing on factor graph, but nonlinearity between the positions and range measurements made the optimization problem complex and multivariate; therefore, the linearization on the Euclidean norm in the range measurement is employed, which ensured that closed-form expression can be obtained to represent the messages on factor. To further improve the closed-form Gaussian representation solutions to nonlinear observation model, the Taylor expansion was used to approximate the nonlinear terms in message updating, while a broadcast message scheme approximating the exact message passing was utilized to reduce the traffic burden in Li et al 28 But what cannot be ignored was that above studies 27,28 did not give an even generalized motion model, namely, the former offered a simple linear state-space model and the later did not mention it; and what was more, they required anchors whether or not accurate although the placement of anchor nodes was normally seen, for example Wymeersch et al 29 SPAWN, a cooperative localization method based on the sum-product algorithm and factor graph, was proposed in Wymeersch et al 29 and applied to ultra-wide bandwidth wireless networks; however, it distinguished between agents with priori unknown states and anchors with known states at all times, both of which may be mobile, and became extremely difficult in directly computing messages. On the whole, some message passing algorithms over factor graphs can be responsive to distributed cooperative localization problems; going a step further, we could consider to design an inference algorithm based on factor graph model to cope with the SLAM problem, which is a relatively less practice to the best of our knowledge.…”