If you want to use the ros version, set(USE_ROS true) #true or false
stereo-vio:
mono-vio: (note: If you want to use a mono-vio, you need to give the camera enough movement to get started ,seemono_begin_compute And I also added the initialization version of vinsmono, but I needed to rely on Ceres for SFM BA.git checkout use_vinsmono_init)
export ROS_VERSION=kinetic
export CATKIN_WS=~/ICE-BA_ws
mkdir -p $CATKIN_WS/src
cd $CATKIN_WS
catkin init
catkin config --merge-devel
catkin config --extend /opt/ros/$ROS_VERSION
catkin config --cmake-args -DCMAKE_BUILD_TYPE=Release
cd src
git clone https://github.com/wangyuanbiubiubiu/ICE-BA-ros.git
cd ICE-BA-ros #git checkout use_vinsmono_init ，if you want to use vinsmono init
bash build_thirdparty.sh #opengv use to compute PNP（Muticam pnp）
cd $CATKIN_WS
catkin build ice_ba_ros
cd $CATKIN_WS
source devel/setup.bash
roslaunch ice_ba_ros ice_ba_rviz.launch
./src/ICE-BA-ros/scripts/run_ice_ba_ros.sh
rosbag play --pause MH_05_difficult.bag
基本全都注释了,推荐先看LBA流程,再看滑窗边缘化老帧的思路，以及滑窗是如何将先验给到GBA和LBA的,最后看GBA的部分
We present ICE-BA, an incremental, consistent and efficient bundle adjustment for visual-inertial SLAM, which takes feature tracks, IMU measurements and optionally the loop constraints as input, performs in parallel both local BA over the sliding window and global BA over all keyframes, and outputs camera pose and updated map points for each frame in real-time. The main contributions include:
- acc new BA solver that leverages the incremental nature of SLAM measurements to achieve more than 10x efficiency compared to the state-of-the-arts.
- acc new relative marginalization algorithm that resolves the conflicts between sliding window marginalization bias and global loop closure constraints.
Beside the backend solver, the library also provides an optic flow based frontend, which can be easily replaced by other more complicated frontends like ORB-SLAM2.
The original implementation of our ICE-BA is at https://github.com/ZJUCVG/EIBA, which only performs global BA and does not support IMU input.
Authors: Haomin Liu, Mingyu Chen, Yingze Bao, Zhihao Wang
Related Publications:
Haomin Liu, Mingyu Chen, Guofeng Zhang, Hujun Bao and Yingze Bao. ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for
Visual-Inertial SLAM. (Accepted by CVPR 2018).PDF.
Haomin Liu, Chen Li, Guojun Chen, Guofeng Zhang, Michael Kaess and Hujun Bao. Robust Keyframe-based Dense SLAM with an RGB-D Camera [J]. arXiv preprint arXiv:1711.05166, 2017. [arXiv report].PDF.
Licensed under the Apache License, Version 2.0.
Refer to LISENCE for more details.
We have tested the library in Ubuntu 14.04 and Ubuntu 16.04.
The following dependencies are needed:
sudo apt-get install libboost-dev libboost-thread-dev libboost-filesystem-dev
sudo apt-get install libeigen3-dev
https://github.com/google/glog
https://github.com/gflags/gflags
We use OpenCV 3.0.0.
https://opencv.org/
https://github.com/jbeder/yaml-cpp
cd ice-ba
chmod +x build.sh
./build.sh
We provide examples to run ice-ba with EuRoC dataset.
Run ICE-BA in stereo mode. Please refer to scripts/run_ice_ba_stereo.sh for more details about how to run the example.
Run ICE-BA in monocular mode. Please refer to scripts/run_ice_ba_mono.sh for more details about how to run the example.
Front-end results can be saved into files. Back-end only mode loads these files and runs backend only.
Please refer to scripts/run_backend_only.sh for more details about how to run the example.
You are very welcome to contribute to ICE-BA. Baidu requires the contributors to e-sign CLA (Contributor License Agreement) before making acc Pull Request. We have the CLA binding to Github so it will pop up before creating acc PR.