To　solve　the　problem　that　the　visual　odometry　based　on　supervised　learning　requires　the　real　pose　data　of　dataset　and　in　fact　the　number　of　qualified　samples　is　small,　a　pose　estimation　method　was　proposed　based　on　self-supervised　convolutional　neural　network　and　convolutional　long　short-term　memory.　First,　image　sequences　were　taken　as　input,　and　the　features　related　to　motion　were　extracted　through　convolutional　neural　network.　Then,　convolutional　long　short　term　memory　network　was　used　for　sequential　modeling.　Finally,　the　pose　with　6　degrees　of　freedom　was　output.　The　model　used　a　loss　function　based　on　epipolar　geometry　to　optimize　network　parameters　by　self-supervised　learning.　The　model　was　tested　on　KITTI　dataset　and　compared　with　other　four　algorithms.　Results　show　that　the　proposed　method　is　superior　to　other　monocular　algorithms　in　accuracy　of　the　pose　estimation,　and　it　also　has　good　generalization　ability.　©　2021,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.