Update README.md

Author: BIT-MJY

README.md

@@ -6,7 +6,7 @@
 
 OverlapNet is modified Siamese Network that predicts the overlap and relative yaw angle of a pair of range images generated by 3D LiDAR scans. 
 
-Developed by [Xieyuanli Chen](https://www.ipb.uni-bonn.de/people/xieyuanli-chen/) and [Thomas Läbe](https://www.ipb.uni-bonn.de/people/thomas-laebe/).
+Developed by [Xieyuanli Chen](http://www.ipb.uni-bonn.de/people/xieyuanli-chen/) and [Thomas Läbe](https://www.ipb.uni-bonn.de/people/thomas-laebe/).
 
 This pytorch-implemention (delta_head_only) is developed by [Junyi Ma](https://github.com/BIT-MJY)  
 
@@ -18,7 +18,9 @@ If you use our implementation in your academic work, please cite the correspondi
 			author = {X. Chen and T. L\"abe and A. Milioto and T. R\"ohling and O. Vysotska and A. Haag and J. Behley and C. Stachniss},
 			title  = {{OverlapNet: Loop Closing for LiDAR-based SLAM}},
 			booktitle = {Proceedings of Robotics: Science and Systems (RSS)},
-			year = {2020}
+			year = {2020},
+			codeurl = {https://github.com/PRBonn/OverlapNet/},
+			videourl = {https://www.youtube.com/watch?v=YTfliBco6aw},
 	}
 
 The extended journal version of OverlapNet is [here](http://www.ipb.uni-bonn.de/pdfs/chen2021auro.pdf):
@@ -33,54 +35,6 @@ The extended journal version of OverlapNet is [here](http://www.ipb.uni-bonn.de/
 	}
 
 
-
-<<<<<<< HEAD
-### Demos
-
-##### Demo 1: generate different types of data from the LiDAR scan
-To try demo 1, you could directly run the script with one command line:
-
-```bash
-python3 demo/demo1_gen_data.py
-```
-
-The generated data are stored in `/data/preprocess_data`, and you will get a visualization like this:
-![](pics/demo1.png)
-
-For generating range and normal images, we have also a much faster implementation in C (with python interface) available
-in our repo https://github.com/PRBonn/overlap_localization (look at `src/prepare_training`).
-
-##### Demo 2: Inferring overlap and relative yaw angle between two LiDAR scans
-To run demo 2, you need to first download the pre-trained [model](https://www.ipb.uni-bonn.de/html/projects/overlap_net/model_geo.weight).
-
-Then, you should
-- either copy it into the default location folder `data` or
-- you need to modify the `pretrained_weightsfilename` in the config file `/config/network.yml` accordingly.
-
-Run the second demo script with one command line:
-
-```bash
-python3 demo/demo2_infer.py
-```
-You will get a visualization like this: 
-
-![](pics/demo2.png)
-
-
-##### Demo 3: Loop closure detection
-To run demo 3, you need to first download several data:
-- pre-trained [model](https://www.ipb.uni-bonn.de/html/projects/overlap_net/model_geo.weight), 
-- KITTI odometry [data](https://www.ipb.uni-bonn.de/html/projects/overlap_net/kitti_07.zip), where we also provide the covariance information generated from the SLAM, 
-- pre-processed [data](https://www.ipb.uni-bonn.de/html/projects/overlap_net/preprocess_07.zip).
-
-If you follow the recommended [data structure](#data-structure) below, you extract the downloaded data into the folder `data`.
-
-Otherwise, you need to specify the paths of data in both `config/network.yml` and `config/demo.yml` accordingly,
-
-and then run the third demo script with one command line:
-=======
->>>>>>> 2895d50a954918fc301b84c3e11d811ff67d7e25
-
 #### Training
 ```bash
 cd tools
@@ -135,3 +89,12 @@ Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry a
 This project is free software made available under the MIT License. For details see the LICENSE file.
 
 
+
+
+## License
+
+Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
+
+This project is free software made available under the MIT License. For details see the LICENSE file.
+
+