通过ncnn部署nanodet实现ORB-SLAM2物体识别及动态物体剔除，针对树莓派等低性能设备开发-毕设/课设/竞赛/实训

# ORB_SLAM2-nanodet **Authors:** shoushou0315 通过ncnn部署nanodet实现ORB-SLAM2物体识别及动态物体剔除，针对树莓派等低性能设备开发。 添加:Detecting类 修改:Frame、FrameDrawer类 ### Related Publications: ORB_SLAM2:https://github.com/raulmur/ORB_SLAM2 ncnn:https://github.com/Tencent/ncnn nanodet:https://github.com/RangiLyu/nanodet ## ncnn 参考https://github.com/Tencent/ncnn 安装ncnn，未测试vulkan所以建议禁用，将ncnn安装到用户主目录即可。 ## 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. **注意版本 0.5！** ## OpenCV We use [OpenCV](http://opencv.org) to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. **建议版本 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](https://github.com/dorian3d/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. ## ROS (optional) We provide some examples to process the live input of a monocular, stereo or RGB-D camera using [ROS](ros.org). Building these examples is optional. In case you want to use ROS, a version Hydro or newer is needed. # yaml添加参数 添加 ``` powersave:0 thread:4 target_size:256 ``` # Building ORB_SLAM2-nanodet library and examples Clone the repository: ``` git clone https://github.com/shoushou0315/ORB_SLAM2-nanodet.git ORB_SLAM2-nanodet ``` Execute: ``` cd ORB_SLAM2-nanodet chmod +x build.sh ./build.sh ``` with ROS: 1. Add the path including *Examples/ROS/ORB_SLAM2-nanodet* to the ROS_PACKAGE_PATH environment variable. Open .bashrc file and add at the end the following line. Replace PATH by the folder where you cloned ORB_SLAM2-nanodet: ``` export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM2-nanodet/Examples/ROS ``` 2. Execute `build_ros.sh` script: ``` chmod +x build_ros.sh ./build_ros.sh ``` ### Running Monocular Node For a monocular input from topic `/camera/image_raw` run node ORB_SLAM2/Mono. You will need to provide the vocabulary file and a settings file. See the monocular examples above. ``` rosrun ORB_SLAM2 Mono PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ``` ### Running Monocular Augmented Reality Demo This is a demo of augmented reality where you can use an interface to insert virtual cubes in planar regions of the scene. The node reads images from topic `/camera/image_raw`. ``` rosrun ORB_SLAM2 MonoAR PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ``` ### Running Stereo Node For a stereo input from topic `/camera/left/image_raw` and `/camera/right/image_raw` run node ORB_SLAM2/Stereo. You will need to provide the vocabulary file and a settings file. If you **provide rectification matrices** (see Examples/Stereo/EuRoC.yaml example), the node will recitify the images online, **otherwise images must be pre-rectified**. ``` rosrun ORB_SLAM2 Stereo PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ONLINE_RECTIFICATION ``` **Example**: Download a rosbag (e.g. V1_01_easy.bag) from the EuRoC dataset (http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets). Open 3 tabs on the terminal and run the following command at each tab: ``` roscore ``` ``` rosrun ORB_SLAM2 Stereo Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml true ``` ``` rosbag play --pause V1_01_easy.bag /cam0/image_raw:=/camera/left/image_raw /cam1/image_raw:=/camera/right/image_raw ``` Once ORB-SLAM2 has loaded the vocabulary, press space in the rosbag tab. Enjoy!. Note: a powerful computer is required to run the most exigent sequences of this dataset. ### Running RGB_D Node For an RGB-D input from topics `/camera/rgb/image_raw` and `/camera/depth_registered/image_raw`, run node ORB_SLAM2/RGBD. You will need to provide the vocabulary file and a settings file. See the RGB-D example above. ``` rosrun ORB_SLAM2 RGBD PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ```