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Super-SLAM

overview

traj

1. License (inherited from ORB-SLAM2)

See LICENSE file. This repo is build on top of https://github.com/KinglittleQ/SuperPoint_SLAM with support for latest pytorch and c++14

C++14

We use the new thread and chrono functionalities of C++14.

Pangolin

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

OpenCV 3

We use OpenCV to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. Required at least OpenCV 3.

Eigen3

Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0.

DBoW3 and g2o (Included in Thirdparty folder)

We use modified versions of DBoW3 (instead of 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.

G++ compiler

Use G++-8 as default to build pytorch

Libtorch

We use Pytorch C++ API to implement SuperPoint model. It can be built with latest pytorch:

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

It may take quite a long time to download and build. Please wait with patience.

NOTE: Do not use the pre-built package in the official website, it would cause some errors.

3. Building SuperPoint-SLAM library and run

chmod +x build.sh
./build.sh

sh run.sh 

4. Download Vocabulary

You can download the vocabulary from google drive or BaiduYun (code: de3g). And then put it into Vocabulary directory. The vocabulary was trained on Bovisa_2008-09-01 using DBoW3 library. Branching factor k and depth levels L are set to 5 and 10 respectively.

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slam + dl keypoint extractors

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