Update README.md

README.md

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+# 1、Visual Odometry or VSLAM
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+# 2、Visual Inertial Odometry or VIO-SLAM
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+# 3、Based CNN（Net VO or Net VSLAM）
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+# 4、Lidar Visual odometry or Lidar SLAM
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+
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 ## OF-VO：Robust and Efficient Stereo Visual Odometry Using Points and Feature Optical Flow
 
 Code：https://github.com/MichaelBeechan/MyStereoLibviso2
 
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 ## SLAMBook
 
 Paper：14 Lectures on Visual SLAM: From Theory to Practice,
@@ -25,6 +37,8 @@ Code：https://github.com/gaoxiang12/slambook
 
 Paper：http://rpg.ifi.uzh.ch/docs/ICRA14_Forster.pdf
 
+Video: http://youtu.be/2YnIMfw6bJY
+
 Code：https://github.com/uzh-rpg/rpg_svo
 
 
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 Code：https://github.com/tum-vision/lsd_slam
 
 
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+## RGBD-Odometry (Visual Odometry based RGB-D images)
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+## Real-Time Visual Odometry from Dense RGB-D Images
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+Code：https://github.com/tzutalin/OpenCV-RgbdOdometry
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+Paper：http://www.computer.org/csdl/proceedings/iccvw/2011/0063/00/06130321.pdf
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+## DVO：Robust Odometry Estimation for RGB-D Cameras 
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+Code：https://github.com/tum-vision/dvo
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+https://vision.in.tum.de/data/software/dvo
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+Paper：https://www.researchgate.net/publication/221430091_Real-time_visual_odometry_from_dense_RGB-D_images
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+## Dense Visual Odometry and SLAM (dvo_slam)
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+Code：https://github.com/tum-vision/dvo_slam
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+https://vision.in.tum.de/data/software/dvo
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+Paper：https://www.researchgate.net/publication/261353146_Dense_visual_SLAM_for_RGB-D_cameras
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+
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+## REVO：Robust Edge-based Visual Odometry 
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+## Combining Edge Images and Depth Maps for Robust Visual Odometry
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+## Robust Edge-based Visual Odometry using Machine-Learned Edges
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+Code：https://github.com/fabianschenk/REVO
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+Paper：https://graz.pure.elsevier.com/
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+
 ## PaoPaoRobot
 
 Code：https://github.com/PaoPaoRobot
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+## Co-Fusion：Real-time Segmentation, Tracking and Fusion of Multiple Objects
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+Paper：http://visual.cs.ucl.ac.uk/pubs/cofusion/index.html
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+## R-VIO：Robocentric Visual-Inertial Odometry
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+Code：https://github.com/rpng/R-VIO
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+Paper：https://arxiv.org/abs/1805.04031
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+
 ## MSCKF_VIO：Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
 
 Paper：https://arxiv.org/abs/1712.00036
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-## VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
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-Paper：https://arxiv.org/abs/1701.08376
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-Code：https://github.com/HTLife/VINet
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 # Computer Vision Group TUM Department of Informatics Technical University of Munich
 
 ## DSO: Direct Sparse Odometry
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 https://www.mdpi.com/2218-6581/7/3/45
 
 
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+## VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
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+Paper：https://arxiv.org/abs/1701.08376
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+Code：https://github.com/HTLife/VINet
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+## DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
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+Code：https://github.com/ildoonet/deepvo
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+https://github.com/sladebot/deepvo
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+https://github.com/themightyoarfish/deepVO
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+https://github.com/fshamshirdar/DeepVO (pytorch)
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+Paper：http://www.cs.ox.ac.uk/files/9026/DeepVO.pdf
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+## UnDeepVO - Implementation of Monocular Visual Odometry through Unsupervised Deep Learning
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+Code：https://github.com/drmaj/UnDeepVO
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+Paper：UnDeepVO - Implementation of Monocular Visual Odometry through Unsupervised Deep Learning
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+
+
+## (ESP-VO) End-to-End, Sequence-to-Sequence Probabilistic Visual Odometry through Deep Neural Networks
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+## https://www.seas.upenn.edu/~meam620/slides/kinematicsI.pdf
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+# Lidar Visual odometry
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+## Lidar-Monocular Visual Odometry
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+Code：https://github.com/johannes-graeter/limo
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+Paper：https://arxiv.org/pdf/1807.07524.pdf
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+## cartographer
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+Code：https://github.com/googlecartographer/cartographer
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+Paper：https://google-cartographer.readthedocs.io/en/latest/
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