A　Gross　Error　Detection　Method　of　Vehicle－borne　Cubic Panoramic　Image　Se quence

Because　the 　epipolar 　geometr y　model　estimation　of 　panoramic 　images 　is 　unstable　under 　the low　match　inlier 　ratio 　caseslar ge　numbers　of 　outliers　or 　errors 　cannot 　be　detected　using　RANSAC method．A　new　gross 　error 　detection　method　based　on　multiple 　constraints 　is 　presented 　for 　vehicleborne　panoramic 　image　se quences．Firstthe 　initial　matching　points 　are 　extracted　using　SIFT　and　nearest 　nei ghbor　matchingthen 　inde pendent 　random　matching　points 　are 　constructed　by　redundant 　gross error 　constraints．Secondthe　movement　relationshi ps　between　panoramic 　images 　are 　approximatel y expressed　by　the　histo gram　statistics 　of 　optical 　flow　magnitude 　and　direction which　can　effectivel y improve 　the　matching　inlier 　ratio．Finall ythe 　epipolar 　geometric 　constraintscale 　constraint 　and　sky point 　constraint 　are 　used 　for 　gross 　error 　detection．Several 　panoramic 　images　were　selected 　and　used for 　experiments．An 　anal ysis 　and　comparison　were　carried　out　on　these　data．The 　results 　show　that the　proposed　method　works　well 　in 　short－baseline　conditions 　for 　the　number　and　accurac y　of 　correct matching　pointses peciall y　for 　complex 　scenes 　in 　low 　inlier 　ratio 　cases．The 　al gorithm　performance 　is relativel y　stableand　provides　better 　constraint 　for 　gross 　errors 　usuall y　caused　by　re peated 　textures scale 　changesand　moving　objects．