Selvaratnam, Daniel D.Shames, ImanRistic, BrankoManton, Jonathan H.2025-05-232025-05-232405-8963http://www.scopus.com/inward/record.url?scp=84991111346&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733753181This paper applies Posterior Cramer-Rao Bound theory to the SLAM problem to measure the information supplied by different sensor modalities over time. Range-only, bearing-only and full range-bearing sensors were considered, as well as the gain in information achieved by using multiple sensors in centralized co-operative SLAM. An efficient recursive formula was used to compute the bound for a set of simulated scenarios, and its validity verified by comparing the bound with the second-order error performance of Fast SLAM 2.0 and the EKF.★★ This research is supported by DST Group under Collaborative ★ This research is supported by DST Group under Collaborative ReTsehairschresAeagrrcehemisenstusppMoYrtIePd b#y59D2S2T, MGYroIuPp #un5d92er3,Caonlldabboyrattihvee Research Agreements MYIP #5922, MYIP #5923, and by the DeesfeenacrcehScAiegnrceeemInesnttistuMteYaIsPan#i5n9i2ti2a,tivMeYoIfPth#e 5S9t2a3te, Ganodverbnymtehnet Defence Science Institute as an initiative of the State Government ofeVfeinccteorSiac.ience Institute as an initiative of the State Government of Victoria. of Victoria.6enPublisher Copyright: © 2016Bayesian boundsbearing-onlycentralizedco-operativeCramer-Rao boundsestimation theoryfilteringlocalisationmap-buildingmulti-agentrange-onlySLAM201610.1016/j.ifacol.2016.07.74684991111346