We implement common slam techniques to reconstruct the RGB-d mapping. Since the method can't deal with movable objects, we are trying to use semantic segmentation method to remove those objects during mapping (person, cat, car, bus ...). The semantic segmentation library used is DeepLab-V2 VGG-16 based model.
The result of common technique of slam:
The result of method combined with deeplab:
The result of technique using deep learning method is heavily based on the accuracy of machine learning model. For example, here we can see that we can't remove all configurations of the person in the scene but we can remove his last configuration. This is because the DeepLab can't recognize this person when just a part of him that can be seen. In the future, we should try some better semantic segmentation model such as DeepLab-V3 or PsPNet.
We also can try real time semantic segmentation model such as ICNet to help with filtering movable objects' features in order to improve PnP RANSAC accuracy and efficiency.
The package is based on OpenCV 3.3, Ceres and DeepLab-V2. The installation process can be found on those links. The DeepLab package supports CPU only devices (I correct the error when compiling CPU only mode and also change the number of outputs of MemoryDataLayer in order to run CRF layer).
Compilation of DeepLab is the same as compiling process as Caffe. Please compile the deeplab package provided in the repository.
After install all of above packages, please copy Ceres and DeepLab cmake files (which should be in the path as /ceres_solver/cmake/ ) in to directoryslam_deeplab/cmake_modules/. Then, compile the package by standard cmake process:
mkdir build cd build cmake .. make
The package was tested on Ubuntu 14.04 and 16.04.
When compiling in the Ubuntu 16.04, please edit src/Cmakelist.txt. Details please see the file.
RGB data should be stored in ./data/rgb_png/ and named as 1.png,2.png. Depth data should be stored in ./data/depth_png/ and named as the same way of RGB images.
parameters.txt is for setting parameters for both two methods. Detail please see the file.
bin/slam is for common method.
bin/slamDP is for learning based method.