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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
SuperPoint + ORB_SLAM2
| Date | Stars |
|---|---|
| 2026-07-24 | 669 |
| 2026-07-25 | 669 |
| 2026-07-28 | 669 |
| 2026-07-30 | 669 |
| 2026-08-06 | 669 |
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# SuperPoint-SLAM **UPDATE: This repo is no longer maintained now. Please refer to https://github.com/jiexiong2016/GCNv2_SLAM if you are intereseted in SLAM with deep learning image descriptors.** **NOTE: SuperPoint-SLAM is not guaranteed to outperform ORB-SLAM. It's just a trial combination of SuperPoint and ORB-SLAM. I release the code for people who wish to do some research about neural feature based SLAM.** This repository was forked from ORB-SLAM2 https://github.com/raulmur/ORB_SLAM2. SuperPoint-SLAM is a modified version of ORB-SLAM2 which use SuperPoint as its feature detector and descriptor. The pre-trained model of SuperPoint come from https://github.com/MagicLeapResearch/SuperPointPretrainedNetwork.   ## 1. License (inherited from ORB-SLAM2) See LICENSE file. ## 2. Prerequisites We have tested the library in **Ubuntu 12.04**, **14.04** and **16.04**, but it should be easy to compile in other platforms. A powerful computer (e.g. i7) will ensure real-time performance and provide more stable and accurate results. ### C++11 or C++0x Compiler We use the new thread and chrono functionalities of C++11. ### Pangolin We use [Pangolin](https://github.com/stevenlovegrove/Pangolin) for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin. ### OpenCV We use [OpenCV](http://opencv.org) to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. **Required at leat 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**. ### DBoW3 and g2o (Included in Thirdparty folder) We use modified versions of [DBoW3](https://github.com/rmsalinas/DBow3) (instead of DBoW2) library to perform place recognition and [g2o](https://github.com/RainerKuemmerle/g2o) library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the *Thirdparty* folder. ### Libtorch We use Pytorch C++ API to implement SuperPoint model. It can be built as follows: ``` shell git clone --recursive -b v1.0.1 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 examples Clone the repository: ``` git clone https://github.com/KinglittleQ/SuperPoint_SLAM.git SuperPoint_SLAM ``` We provide a script `build.sh` to build the *Thirdparty* libraries and *SuperPoint_SLAM*. Please make sure you have **installed all required dependencies** (see section 2). Execute: ``` cd SuperPoint_SLAM chmod +x build.sh ./build.sh ``` This will create **libSuerPoint_SLAM.so** at *lib* folder and the executables **mono_tum**, **mono_kitti**, **mono_euroc** in *Examples* folder. **TIPS:** If cmake cannot find some package such as OpenCV or EIgen3, try to set XX_DIR which contain XXConfig.cmake manually. Add the following statement into `CMakeLists.txt` before `find_package(XX)`: ``` cmake set(XX_DIR "your_path") # set(OpenCV_DIR "usr/share/OpenCV") # set(Eigen3_DIR "usr/share/Eigen3") ``` ## 4. Download Vocabulary You can download the vocabulary from [google drive](https://drive.google.com/file/d/1p1QEXTDYsbpid5ELp3IApQ8PGgm_vguC/view?usp=sharing) or [BaiduYun](https://pan.baidu.com/s/1fygQil78GpoPm0zoi6BMng) (code: de3g). And then put it into `Vocabulary` directory. The vocabulary was trained on [Bovisa_2008-09-01](http://www.rawseeds.org/rs/datasets/view//7) using DBoW3 library. Branching factor k and depth levels L are set to 5 and 10 respectively. ## 5. Monocular Examples ### KITTI Dataset 1. Download the dataset (grayscale images) from http://www.cvli
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Chengqi Deng · Zhejiang University · China
17
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1b6ca6eff5de3a8e, topic:slam, name:slam, readme:slam