GCNv2 SLAM

Forked from https://github.com/jiexiong2016/GCNv2_SLAM

0.1、代码仓库介绍

GCNv2 是替换 orbslam 中 orb 特征点的深度学习特征点，用于解决相机晃动过大时位姿丢失的问题。

0.2、更新日志

修改为 cpu 模式

新增位姿发布功能

在原版代码基础上适配了工控机系统编译环境

0.3、使用说明

见 use_gcn.md

0.4、使用经验

工控机 cpu 上仅能达到 3fps ，无法实时计算
单目性能不如 orb 特征点，rgbd 性能优于 orb 特征点
不建议在机器上使用

Introduction

GCNv2 is a high-throughput variant of the Geometric Correspondence Network for performing RGB-D SLAM online on embedded platforms. We trained the binary descriptor in the same format with ORB (32 bytes) for the convenience of integration. In this implementation, we evaluate the motion estimation using a system built on top the [ORB-SLAM2], (https://github.com/raulmur/ORB_SLAM2). Thanks to the robustness of ORB-SLAM2, our system is able to achive reliable tracking perfomance on our drone platform in real-time.

Example

Online running performance with ORB and GCNv2 features:

ORB:

GCNv2:

Related Publications

GCNv2: Efficient Correspondence Prediction for Real-Time SLAM, J. Tang, L. Ericson, J. Folkesson and P. Jensfelt, in arXiv:1902.11046, 2019
Geometric Correspondence Network for Camera Motion Estimation, J. Tang, J. Folkesson and P. Jensfelt, RA-L and ICRA 2018

Dependencies

C++11 or C++0x Compiler

We use the new thread and chrono functionalities of C++11.

Pytorch

We use Pytorch C++ api(libtorch) for deloying the GCNv2. The libtorch can be built as follows:

git clone --recursive -b v1.0.1 https://github.com/pytorch/pytorch
cd pytorch && mkdir build && cd build
python ../tools/build_libtorch.py

The built libtorch library is located at pytorch/torch/lib/tmp_install/ in default.

Update: Have added support for master branch of pytorch or version larger than 1.0.1. For newer version, set TORCH_PATH to pytorch/torch/share/cmake/Torch

Required at least 1.0.1. Lower version of pytorch has cuDNN linking issue:pytorch/pytorch#14033 (comment).

Plese avoid using the pre-built version of libtorch since it will cause linking errors (due to CXX11 ABI issue).

Pangolin

We use Pangolin for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.

OpenCV

We use OpenCV to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org.

Required at least 2.4.3. Tested with OpenCV 2.4.11 and OpenCV 3.2.

Eigen3

Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org.

Required at least 3.1.0.

DBoW2 and g2o (Included in Thirdparty folder)

We use modified versions of the DBoW2 library to perform place recognition and g2o library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the Thirdparty folder.

Preparation

Clone the code

git clone https://github.com/jiexiong2016/GCNv2_SLAM.git

Then build the project

cd GCNv2_SLAM
./build.sh

Make sure to edit build.sh pointing to your local libtorch installation. Edit run.sh to check out how to run with GCNv2 or vanilla ORB. Check the Network.md for the network structure and link for trained models.

Image resolution

Update Set "FULL_RESOLUTION=1" and use "gcn2_640x480.pt" to test with image resolution "640x480" intead. The input image size should be consitent with the model to be used.

cyizhuo/GCNv2_SLAM_modified