Aiming　at　solving　the　problem　of　timestamp　misalignment　of　dynamic　exposure　monocular　visual-inertial　system　(VINS),　a　timestamp　delay　estimation　method　was　proposed.　First,　different　factors,　which　lead　to　the　timestamp　delay,　such　as　sensor　exposure　time,　internal　sensor　latency,　filtering　and　data　transmission,　were　analyzed.　With　the　above　analysis,　the　dynamic　exposure　timestamp　delay　model　was　established.　Second,　based　on　the　B-spline　curve　fitting　and　cross-correlation,　a　rough　estimation　of　the　time　delay　was　obtained.　Then,　the　objective　function　was　constructed　and　optimized　by　nonlinear　optimization　method　to　obtain　the　accurate　estimation　of　the　timestamp　delays　corresponding　to　different　exposure　times.　Finally,　the　parameters　of　the　dynamic　exposure　timestamp　delay　model　were　obtained　by　fitting　the　estimated　results　to　the　proposed　model　with　least　squares.　The　real　physical　experimental　results　show　that　the　proposed　dynamic　exposure　timestamp　delay　model　is　consistent　with　the　actual　physical　hardware　time　delay　characteristic,　and　the　estimation　accuracy　of　visual-inertial　odometry　is　improved　after　compensating　the　timestamp　delay　with　the　proposed　model,　which　can　provide　more　accurate　data　for　vision-based　localization,　navigation,　mapping　and　path　planning.　©　2019,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.