Multi-Step Prediction of Occupancy Grid Maps With Recurrent Neural Networks
暂无分享,去创建一个
[1] Gabriel Kreiman,et al. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning , 2016, ICLR.
[2] Sebastian Thrun,et al. Model based vehicle detection and tracking for autonomous urban driving , 2009, Auton. Robots.
[3] Steven Lake Waslander,et al. State initialization for recurrent neural network modeling of time-series data , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[4] Kazuya Yoshida,et al. Collision avoidance method for mobile robot considering motion and personal spaces of evacuees , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[5] Sen Wang,et al. End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks , 2018, Int. J. Robotics Res..
[6] Klaus C. J. Dietmayer,et al. A random finite set approach for dynamic occupancy grid maps with real-time application , 2016, Int. J. Robotics Res..
[7] Klaus Dietmayer,et al. Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[8] Yann LeCun,et al. Deep multi-scale video prediction beyond mean square error , 2015, ICLR.
[9] Dushyant Rao,et al. Deep tracking in the wild: End-to-end tracking using recurrent neural networks , 2018, Int. J. Robotics Res..
[10] A. Elfes,et al. Occupancy Grids: A Stochastic Spatial Representation for Active Robot Perception , 2013, ArXiv.
[11] Kelson Rômulo Teixeira Aires,et al. An Approach for 2D Visual Occupancy Grid Map Using Monocular Vision , 2011, CLEI Selected Papers.
[12] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[13] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[14] Gunnar Farnebäck,et al. Two-Frame Motion Estimation Based on Polynomial Expansion , 2003, SCIA.
[15] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[16] Nitish Srivastava,et al. Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.
[17] Ingmar Posner,et al. Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks , 2016, AAAI.
[18] Mark E. Campbell,et al. A mixture‐model based algorithm for real‐time terrain estimation , 2006, J. Field Robotics.
[19] Tomomasa Sato,et al. Mobile robot path planning using human prediction model based on massive trajectories , 2012, 2012 Ninth International Conference on Networked Sensing (INSS).
[20] Luc Van Gool,et al. Object Detection and Tracking for Autonomous Navigation in Dynamic Environments , 2010, Int. J. Robotics Res..
[21] Zhou Wang,et al. Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.
[22] Ray A. Jarvis,et al. Optimal Global Path Planning in Time Varying Environments Based on a Cost Evaluation Function , 2008, Australasian Conference on Artificial Intelligence.
[23] Klaus C. J. Dietmayer,et al. Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).
[24] Sergey Levine,et al. Unsupervised Learning for Physical Interaction through Video Prediction , 2016, NIPS.
[25] Emmanouil Tsardoulias,et al. A Review of Global Path Planning Methods for Occupancy Grid Maps Regardless of Obstacle Density , 2016, J. Intell. Robotic Syst..
[26] Steven Lake Waslander,et al. Deep Learning a Quadrotor Dynamic Model for Multi-Step Prediction , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[27] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.