Dynamic　object　detection　and　tracking　from　complex　scenes　is　a　challenge　in　the　field　of　robot　vision.　In　this　paper,　we　exploit　a　laser　scanner　associated　with　a　camera　to　achieve　the　map　reconstruction,　removing　and　tracking　dynamic　objects.　Laser-based　and　image-based　dynamic　detection　method　are　proposed,　and　their　quality　is　analyzed　respectively.　Efficiency　and　accuracy　are　enhanced　by　a　hybrid　method　associated　3D　and　2D　information.　A　3D　Gaussian　mixture　probability　hypothesis　density-based　(GM-PHD)　filter　is　designed　to　track　the　motion　trajectories　of　multiple　dynamic　objects.　The　experimental　result　conducted　on　KITTI　public　dataset　show　the　effectiveness　of　our　method.