This　paper　presents　an　optical　flow-based　novel　technique　to　perceive　the　instant　motion　velocity　of　a　smart　wheelchair　robot.　The　primary　focus　of　this　study　is　to　determine　the　wheelchair＇s　ego-motion　using　a　displacement　field　in　temporally　consecutive　image　pairs.　In　contrast　to　most　previous　approaches　for　estimating　velocity,　the　proposed　strategy　has　two　main　innovations.　Firstly,　the　proposed　tilted　overlooking　camera　set-up　instead　of　conventional　downward-looking　camera　and　the　corresponding　ego-motion　model　is　presented　for　compact　indoor　mobile　robots.　Secondly,　by　virtue　of　the　graphic　processing　unit-accelerated　TV-L-1　algorithm,　which　is　coupled　with　motion　priors-based　pixel　prediction,　we　are　permitted　to　improve　the　accuracy　and　efficiency　of　the　optical　flow　estimation　significantly.　In　order　to　render　our　method　more　robust　with　respect　to　noise　and　outliers,　we　propose　a　quadratic　motion　model-based　random　sample　consensus　(RANSAC)　refinement　of　flow　fields.　Advantages　of　our　proposal　are　validated　by　real　experimental　results　carried　on　our　smart　wheelchair　platform　and　contrast　evaluations　conducted　on　Pioneer　robot.