Detecting　and　tracking　dynamic　objects　in　a　scene　using　point　cloud　data　collected　by　LiDAR　and　estimating　the　motion　state　of　objects　with　high　accuracy　are　challenges　for　autonomous　driving　technology.　In　this　study,　a　motion　detection　method　based　on　point　cloud　registration　is　investigated　to　detect　motion　through　the　overlapping　relationship　between　source　and　target　point　clouds　after　registration　and　extract　moving　objects　using　clustering　and　scale　analysis　by　combining　the　object　information　of　interest　acquired　by　deep　learning　networks.　Next,　object　association　is　achieved　by　object　motion　information　and　geometric　and　texture　features.　Then,　a　point　cloud　registration　method　flow　is　designed　to　estimate　the　motion　state　of　the　object　with　high　accuracy　by　point　cloud　registration.　The　detection,　tracking　and　estimation　of　the　accurate　motion　state　of　moving　objects　are　achieved.