This　paper　considers　the　problem　of　merging　grid　maps　that　have　different　resolutions.　Because　the　goal　of　map　merging　is　to　find　the　optimal　transformation　between　two　partially　overlapping　grid　maps,　it　can　be　viewed　as　a　special　image　registration　issue.　To　address　this　special　issue,　the　solution　considers　the　non-common　areas　and　designs　an　objective　function　based　on　the　trimmed　mean-square　error　(MSE).　The　trimmed　and　scaling　iterative　closest　point　(TsICP)　algorithm　is　then　proposed　to　solve　this　well-designed　objective　function.　As　the　TsICP　algorithm　can　be　proven　to　be　locally　convergent　in　theory,　a　good　initial　transformation　should　be　provided.　Accordingly,　scale-invariant　feature　transform　(SIFT)　features　are　extracted　for　the　maps　to　be　potentially　merged,　and　the　random　sample　consensus　(RANSAC)　algorithm　is　employed　to　find　the　geometrically　consistent　feature　matches　that　are　used　to　estimate　the　initial　transformation　for　the　TsICP　algorithm.　In　addition,　this　paper　presents　the　rules　for　the　fusion　of　the　grid　maps　based　on　the　estimated　transformation.　Experimental　results　carried　out　with　publicly　available　datasets　illustrate　the　superior　performance　of　this　approach　at　merging　grid　maps　with　respect　to　robustness　and　accuracy.