update the whole readme

README.md

@@ -1,54 +1,115 @@
-# signal-image-keypoints-odom
-## WILL DO THE README UPDATE SOON
-## Installation
+# Signal-image-keypoints-odom
+
+
+![Alt text](/imgs/Kiss_55_sup.png)
+
+## Introduction
+We present a novel approach to estimate the LiDAR-based odometry by using signal image keypoints. 
+
+Our work contributes in three main aspects:
+
+* We conduct an extensive study of the optimal resolution and interpolation approaches for enhancing the low-resolution LiDAR-generated data to extract keypoints more effectively.
+
+* We investigate the efficacy of the existing keypoint detectors and descriptors on LiDAR-generated images with multiple specialized metrics providing a quantitative evaluation.
+
+  For more details on these two aspects, please refer to this [folder](detector_descriptor_for_LiDARimg), which contains detailed introductions and experimental results.
+
+* We propose a novel approach that leverages the detected keypoints and their neighbors to extract a reliable point cloud (downsampling) for the purpose of point cloud registration with reduced computational overhead and fewer deficiencies in valuable point acquisition.
+
+For more details, please refer to our paper: [LiDAR-Generated Images Derived Keypoints Assisted Point Cloud Registration Scheme in Odometry Estimation](https://www.mdpi.com/2072-4292/15/20/5074).
+
+
+## Repository Structure
+- `detector_descriptor_for_LiDARimg`: The first two contributions of our work, firstly, we explore a preprocessing method for enhancing low-resolution LiDAR-generated data to extract keypoints more effectively. Second and more important, we assess the effectiveness of the current keypoint detectors and descriptors on LiDAR-generated images using multiple specialized metrics. Please refer to the more detailed [README](detector_descriptor_for_LiDARimg/README.md) inside the folder.
+- `src`: 
+    * signal-image-keypoints-odom: Source code of our new odometry approach.
+    * libs: libtorch--The C++ version for Pytorch.
+    * kiss-icp: The original kiss-icp (After you run 'git submodule update --init --recursive')
+    * cpu_monitor: A cpu monitor for monitoring the performance of our method. (After you run 'git submodule update --init --recursive')
+
+
+- `scripts`: several python scripts to print out the CPU and memory usage of our method. you don't have to run them.
+
+
+And the original rosbag dataset we used is available at the [University of Turku servers](https://utufi.sharepoint.com/:f:/s/msteams_0ed7e9/Etwsa7m8hxhMk9H3x-K6DfUBgU3x-ZK9vMeD_V0J2mdHwA).
+
+
+
+
+## Run our code
 
 ### Clone our repo
 ```
 git clone [email protected]:RealYXJ/ws-lidar-as-camera-odom.git
 cd ws-lidar-as-camera-odom
-git checkout optimize_code_zhz
-git submodule update --init --recursive
+```
+### Update the submodules
 
+Since we have use some submodules, so we need to update the submodules.
 ```
+git submodule update --init --recursive
+```
+
+### Install libstorch
 
-### Install Dependency
 ```
-<!-- libtorch -->
 cd ws-lidar-as-camera-odom/src/ && mkdir libs && cd libs
+
 wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-1.11.0%2Bcpu.zip
+
 unzip libtorch-cxx11-abi-shared-with-deps-1.11.0+cpu.zip
+
 rm libtorch-cxx11-abi-shared-with-deps-1.11.0+cpu.zip
 ```
+
 Note: The lastest version will cause problems like unference to ros::init even though I have linked with `${catkin_LIBRARIES}`. Same problem has met by other people in https://github.com/pytorch/pytorch/issues/60178. I followed the solution provided by mmurooka commented on Nov 2, 2022 (https://github.com/mmurooka/SMPLpp/issues/1). Issue solved.
 
-## Remember to check several files to make sure that we have the right absolute path
 
-## cmake build oand run our project
-```
-cd ws-lidar-as-camera-odom/
-catkin build
+### Other requirements
+
+The code has been tested on Ubuntu 20.04 with ROS Noetic. And we use the following common libraries. You can check the [CMakeLists.txt](src/signal-image-keypoints-odom/CMakeLists.txt) for more details.
+
+* opencv
+* pcl
+
+
+### Make sure you have the right path
+
+
+We found that there is a path in our code, it has to be the absolute path, so you need to change it to your own path.
+This line of code is inside the [signal_image_keypoints_odom.hpp](src/signal-image-keypoints-odom/include/signal_image_keypoints_odom/signal_image_keypoints_odom.hpp) file.
+
+Line 79:
+```cpp
+  std::string weight_path_ = "/home/jimmy/Downloads/ws-lidar-as-camera-odom/src/signal-image-keypoints-odom/model/superpoint_v2.pt";
 ```
 
 
-## Optimize step 
+### cmake build oand run our project
 ```
 cd ws-lidar-as-camera-odom/
+catkin build
 source devel/setup.bash
 ```
 
 
-Run the keypoint extractor ros node
+### Run the keypoint extractor ros node
 ```
 roslaunch signal-image-keypoints-odom signal-feature-odom.launch
 ```
-Run the pointcloud matching approach for the odometry, or you can choose topic:=//os_cloud_node/points
+
+
+### Run the pointcloud matching approach 
+
+Run the pointcloud matching approach for the odometry ( or you can choose topic:=//os_cloud_node/points, to try the kiss-icp with raw point cloud)
 
 ```
 roslaunch kiss_icp odometry.launch topic:=/keypoint_point_cloud
 ```
 
-Run CPU_monitor
 
+### Run CPU_monitor
+Run CPU_monitor (You don't have to run it, it's just for monitoring the CPU and memory usage of our method)
 ```
 roslaunch cpu_monitor cpu_monitor.launch poll_period:=1
 ```
@@ -59,44 +120,33 @@ Run python scripts to show CPU and memory usage of our nodes, cpu_mem2.py will p
 python cpu_mem2.py 
 ```
 
-Play the rosbag
+### Play the rosbag
 
 ```
 rosbag play [rosbag that you have]
 ```
 
-### What I modified:
-1. delele several useless variable and function inside the class
-1. rearrange the class member,change the name of many function and variable, to make them more clear
-2. inside pointCloudCallback function, can just use one line: pcl::fromROSMsg(*msg, *entirePointCloudPtr_);
-3. unify the signalImageCallback and rangeImageCallback function, just use cv_bridge::toCvShare, no need to use cv_bridge::toCvCopy, because we always copy the variable inside the superpoint_timerCallback function
-4. turn extract_neighbourhood into a class member function， so increase encapsulation
-5. threshold inside the extract_neighbourhood, is weird, 300 will not work as we want, because the image range in between [0,1]
-6. we can just use signal_mp.convertTo(signal_mp, CV_32F, 1.0 / 65535.0), it's more loseless compared to the previous one.
-7. if (u>SH_ || v > SW_)....... these if situations will not happen, so we don't need them.
-8. add cpu_monitor to submodule
-
-
-### KISS-ICP with raw point cloud
-
-![](./imgs/kiss-icp-raw.png)
-
-### KISS-ICP with conventional keypoint extractors
-
-![](./imgs/kiss-icp-a.png)
-
-### KISS-ICP with the superpoint feature extractor
-
-
-
-
-### KISS-ICP with raw point cloud
-
-![](./imgs/kiss-icp-raw.png)
+## Citation
+If you find our work useful in your research, please consider citing:
+```
+@Article{rs15205074,
+AUTHOR = {Zhang, Haizhou and Yu, Xianjia and Ha, Sier and Westerlund, Tomi},
+TITLE = {LiDAR-Generated Images Derived Keypoints Assisted Point Cloud Registration Scheme in Odometry Estimation},
+JOURNAL = {Remote Sensing},
+VOLUME = {15},
+YEAR = {2023},
+NUMBER = {20},
+ARTICLE-NUMBER = {5074},
+URL = {https://www.mdpi.com/2072-4292/15/20/5074},
+ISSN = {2072-4292},
+DOI = {10.3390/rs15205074}
+}
+```
 
-### KISS-ICP with conventional keypoint extractors
+## License
+Distributed under the MIT License. See LICENSE for more information.
 
-![](./imgs/kiss-icp-a.png)
+## Contact
+For any queries regarding the code or the paper, please open an issue in this repository.
 
-### KISS-ICP with the superpoint feature extractor
 

src/signal-image-keypoints-odom/CMakeLists.txt

@@ -24,7 +24,7 @@ message("    libraries:" ${OpenCV_LIBS})
 message("    include path:" ${OpenCV_INCLUDE_DIRS})
 
 # find torch 
-set(CMAKE_PREFIX_PATH "/home/jimmy/Downloads/Performance_comparison/ws-lidar-as-camera-odom/src/libs/libtorch/share/cmake/Torch")
+set(CMAKE_PREFIX_PATH "../libs/libtorch/share/cmake/Torch")
 find_package(Torch REQUIRED)
 
 # include pcl libraries

src/signal-image-keypoints-odom/include/signal_image_keypoints_odom/signal_image_keypoints_odom.hpp

@@ -76,7 +76,7 @@ class lidarImageKeypointOdom
 
 
 
-    std::string weight_path_ = "/home/jimmy/Downloads/Performance_comparison/ws-lidar-as-camera-odom/src/signal-image-keypoints-odom/model/superpoint_v2.pt";
+    std::string weight_path_ = "/home/jimmy/Downloads/ws-lidar-as-camera-odom/src/signal-image-keypoints-odom/model/superpoint_v2.pt";
     SPFrontend spfrontend_{weight_path_, 4, 0.015, 0.4};