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Object residual constrained Visual-Inertial Odometry (OrcVIO) is a visual-inertial odometry pipeline, which is tightly coupled with tracking and optimization over structured object models. It provides accurate trajectory estimation and large-scale object-level mapping from online Stereo+IMU data.
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OrcVIO-Lite only uses bounding boxs and no keypoints. The object mapping module and VIO module are implemented in separate ROS nodelets and are decoupled.
@inproceedings{shan2020orcvio,
title={OrcVIO: Object residual constrained Visual-Inertial Odometry},
author={Shan, Mo and Feng, Qiaojun and Atanasov, Nikolay},
booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={5104--5111},
year={2020},
organization={IEEE}
}
This repository was tested on Ubuntu 18.04 with ROS Melodic.
The core algorithm depends on Eigen, Boost, Suitesparse, Ceres, OpenCV, Sophus, GTest
- Environment is
Ubuntu 18.04with ROSMelodic - The ROS version also depends on catkin simple, please put it in the
ros_wrapper/srcfolder
$ git clone --recursive https://github.com/shanmo/OrcVIO-Stereo-Mapping.git
$ cd OrcVIO-Stereo-Mapping/ros_wrapper
$ catkin_make
$ source ./devel/setup.bash
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Download ERL indoor hand-held dataset (chairs), which was collected with Realsense D455 in Existential Robotics Lab, University of California San Diego
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Please refer to wiki regarding how to setup D455
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Run
roslaunch orcvio orcvio_d455.launchgenerates the result below
- Download ERL indoor robotic car dataset, change the rosbag path, and run
roslaunch orcvio orcvio_d455.launchto get the demos like below
MIT License
Copyright (c) 2021 ERL at UCSD