首页|Recent Studies from University of Essex Add New Data to Computational Intelligen ce (Rstnet: Recurrent Spatial-temporal Networks for Estimating Depth and Ego-mot ion)
Recent Studies from University of Essex Add New Data to Computational Intelligen ce (Rstnet: Recurrent Spatial-temporal Networks for Estimating Depth and Ego-mot ion)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing - Computational Intelligence have been published. According to news reportin g out of Essex, United Kingdom, by NewsRx editors, researchstated, “Depth map a nd ego-motion estimations from monocular consecutive images are challenging tou nsupervised learning Visual Odometry (VO) approaches. This paper proposes a nove l VO architecture:Recurrent Spatial-Temporal Network (RSTNet), which can estima te the depth map and ego-motion frommonocular consecutive images.”
EssexUnited KingdomEuropeComputati onal IntelligenceEmerging TechnologiesMachine LearningUnsupervised Learnin gUniversity of Essex
NETL
NSTL
