pi/ORB-SLAM3_Linux
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

merge readme

Browse Source
This commit is contained in:
Ivan
2022-06-25 19:34:10 +03:00
parent bbabd50d1e 2c8b5327bf
commit 594418420d
1 changed files with 20 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
README.md
View File

@@ -1,17 +1,19 @@
<<<<<<< HEAD
# ORB-SLAM3 # ORB-SLAM3
The ORB-SLAM3 system with changes made for specific tasks at DriveCast.
### V1.0, December 22th, 2021 ### V1.0, December 22th, 2021
**Authors:** Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, [José M. M. Montiel](http://webdiis.unizar.es/~josemari/), [Juan D. Tardos](http://webdiis.unizar.es/~jdtardos/). **Authors:** Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, [José M. M. Montiel](http://webdiis.unizar.es/~josemari/), [Juan D. Tardos](http://webdiis.unizar.es/~jdtardos/).
The [Changelog](https://github.com/UZ-SLAMLab/ORB_SLAM3/blob/master/Changelog.md) describes the features of each version. The [Changelog](https://github.com/UZ-SLAMLab/ORB_SLAM3/blob/master/Changelog.md) describes the features of each version.
ORB-SLAM3 is the first real-time SLAM library able to perform **Visual, Visual-Inertial and Multi-Map SLAM** with **monocular, stereo and RGB-D** cameras, using **pin-hole and fisheye** lens models. In all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature, and significantly more accurate. ORB-SLAM3 is the first real-time SLAM library able to perform **Visual, Visual-Inertial and Multi-Map SLAM** with **monocular, stereo and RGB-D** cameras, using **pin-hole and fisheye** lens models. In all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature, and significantly more accurate.
We provide examples to run ORB-SLAM3 in the [EuRoC dataset](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) using stereo or monocular, with or without IMU, and in the [TUM-VI dataset](https://vision.in.tum.de/data/datasets/visual-inertial-dataset) using fisheye stereo or monocular, with or without IMU. Videos of some example executions can be found at [ORB-SLAM3 channel](https://www.youtube.com/channel/UCXVt-kXG6T95Z4tVaYlU80Q). We provide examples to run ORB-SLAM3 in the [EuRoC dataset](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) using stereo or monocular, with or without IMU, and in the [TUM-VI dataset](https://vision.in.tum.de/data/datasets/visual-inertial-dataset) using fisheye stereo or monocular, with or without IMU. Videos of some example executions can be found at [ORB-SLAM3 channel](https://www.youtube.com/channel/UCXVt-kXG6T95Z4tVaYlU80Q).
This software is based on [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2) developed by [Raul Mur-Artal](http://webdiis.unizar.es/~raulmur/), [Juan D. Tardos](http://webdiis.unizar.es/~jdtardos/), [J. M. M. Montiel](http://webdiis.unizar.es/~josemari/) and [Dorian Galvez-Lopez](http://doriangalvez.com/) ([DBoW2](https://github.com/dorian3d/DBoW2)). This software is based on [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2) developed by [Raul Mur-Artal](http://webdiis.unizar.es/~raulmur/), [Juan D. Tardos](http://webdiis.unizar.es/~jdtardos/), [J. M. M. Montiel](http://webdiis.unizar.es/~josemari/) and [Dorian Galvez-Lopez](http://doriangalvez.com/) ([DBoW2](https://github.com/dorian3d/DBoW2)).
<a href="https://youtu.be/HyLNq-98LRo" target="_blank"><img src="https://img.youtube.com/vi/HyLNq-98LRo/0.jpg" <a href="https://youtu.be/HyLNq-98LRo" target="_blank"><img src="https://img.youtube.com/vi/HyLNq-98LRo/0.jpg"
alt="ORB-SLAM3" width="240" height="180" border="10" /></a> alt="ORB-SLAM3" width="240" height="180" border="10" /></a>
### Related Publications: ### Related Publications:
@@ -22,7 +24,7 @@ alt="ORB-SLAM3" width="240" height="180" border="10" /></a>
[ORBSLAM-Atlas] Richard Elvira, J. M. M. Montiel and Juan D. Tardós, **ORBSLAM-Atlas: a robust and accurate multi-map system**, *IROS 2019*. **[PDF](https://arxiv.org/pdf/1908.11585.pdf)**. [ORBSLAM-Atlas] Richard Elvira, J. M. M. Montiel and Juan D. Tardós, **ORBSLAM-Atlas: a robust and accurate multi-map system**, *IROS 2019*. **[PDF](https://arxiv.org/pdf/1908.11585.pdf)**.
[ORBSLAM-VI] Raúl Mur-Artal, and Juan D. Tardós, **Visual-inertial monocular SLAM with map reuse**, IEEE Robotics and Automation Letters, vol. 2 no. 2, pp. 796-803, 2017. **[PDF](https://arxiv.org/pdf/1610.05949.pdf)**. [ORBSLAM-VI] Raúl Mur-Artal, and Juan D. Tardós, **Visual-inertial monocular SLAM with map reuse**, IEEE Robotics and Automation Letters, vol. 2 no. 2, pp. 796-803, 2017. **[PDF](https://arxiv.org/pdf/1610.05949.pdf)**.
[Stereo and RGB-D] Raúl Mur-Artal and Juan D. Tardós. **ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras**. *IEEE Transactions on Robotics,* vol. 33, no. 5, pp. 1255-1262, 2017. **[PDF](https://arxiv.org/pdf/1610.06475.pdf)**. [Stereo and RGB-D] Raúl Mur-Artal and Juan D. Tardós. **ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras**. *IEEE Transactions on Robotics,* vol. 33, no. 5, pp. 1255-1262, 2017. **[PDF](https://arxiv.org/pdf/1610.06475.pdf)**.
@@ -37,13 +39,13 @@ ORB-SLAM3 is released under [GPLv3 license](https://github.com/UZ-SLAMLab/ORB_SL
For a closed-source version of ORB-SLAM3 for commercial purposes, please contact the authors: orbslam (at) unizar (dot) es. For a closed-source version of ORB-SLAM3 for commercial purposes, please contact the authors: orbslam (at) unizar (dot) es.
If you use ORB-SLAM3 in an academic work, please cite: If you use ORB-SLAM3 in an academic work, please cite:
@article{ORBSLAM3_2020, @article{ORBSLAM3_2020,
title={{ORB-SLAM3}: An Accurate Open-Source Library for Visual, Visual-Inertial title={{ORB-SLAM3}: An Accurate Open-Source Library for Visual, Visual-Inertial
and Multi-Map {SLAM}}, and Multi-Map {SLAM}},
author={Campos, Carlos AND Elvira, Richard AND G\´omez, Juan J. AND Montiel, author={Campos, Carlos AND Elvira, Richard AND G\´omez, Juan J. AND Montiel,
Jos\'e M. M. AND Tard\'os, Juan D.}, Jos\'e M. M. AND Tard\'os, Juan D.},
journal={IEEE Transactions on Robotics}, journal={IEEE Transactions on Robotics},
volume={37}, volume={37},
number={6}, number={6},
pages={1874-1890}, pages={1874-1890},
@@ -100,7 +102,7 @@ This will create **libORB_SLAM3.so** at *lib* folder and the executables in *Ex
1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets 1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
2. Open the script "euroc_examples.sh" in the root of the project. Change **pathDatasetEuroc** variable to point to the directory where the dataset has been uncompressed. 2. Open the script "euroc_examples.sh" in the root of the project. Change **pathDatasetEuroc** variable to point to the directory where the dataset has been uncompressed.
3. Execute the following script to process all the sequences with all sensor configurations: 3. Execute the following script to process all the sequences with all sensor configurations:
``` ```
@@ -120,7 +122,7 @@ Execute the following script to process sequences and compute the RMS ATE:
1. Download a sequence from https://vision.in.tum.de/data/datasets/visual-inertial-dataset and uncompress it. 1. Download a sequence from https://vision.in.tum.de/data/datasets/visual-inertial-dataset and uncompress it.
2. Open the script "tum_vi_examples.sh" in the root of the project. Change **pathDatasetTUM_VI** variable to point to the directory where the dataset has been uncompressed. 2. Open the script "tum_vi_examples.sh" in the root of the project. Change **pathDatasetTUM_VI** variable to point to the directory where the dataset has been uncompressed.
3. Execute the following script to process all the sequences with all sensor configurations: 3. Execute the following script to process all the sequences with all sensor configurations:
``` ```
@@ -128,7 +130,7 @@ Execute the following script to process sequences and compute the RMS ATE:
``` ```
## Evaluation ## Evaluation
In TUM-VI ground truth is only available in the room where all sequences start and end. As a result the error measures the drift at the end of the sequence. In TUM-VI ground truth is only available in the room where all sequences start and end. As a result the error measures the drift at the end of the sequence.
Execute the following script to process sequences and compute the RMS ATE: Execute the following script to process sequences and compute the RMS ATE:
``` ```
@@ -149,14 +151,14 @@ and add at the end the following line. Replace PATH by the folder where you clon
``` ```
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM3/Examples/ROS export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM3/Examples/ROS
``` ```
2. Execute `build_ros.sh` script: 2. Execute `build_ros.sh` script:
``` ```
chmod +x build_ros.sh chmod +x build_ros.sh
./build_ros.sh ./build_ros.sh
``` ```
### Running Monocular Node ### Running Monocular Node
For a monocular input from topic `/camera/image_raw` run node ORB_SLAM3/Mono. You will need to provide the vocabulary file and a settings file. See the monocular examples above. For a monocular input from topic `/camera/image_raw` run node ORB_SLAM3/Mono. You will need to provide the vocabulary file and a settings file. See the monocular examples above.
@@ -168,7 +170,7 @@ For a monocular input from topic `/camera/image_raw` run node ORB_SLAM3/Mono. Yo
For a monocular input from topic `/camera/image_raw` and an inertial input from topic `/imu`, run node ORB_SLAM3/Mono_Inertial. Setting the optional third argument to true will apply CLAHE equalization to images (Mainly for TUM-VI dataset). For a monocular input from topic `/camera/image_raw` and an inertial input from topic `/imu`, run node ORB_SLAM3/Mono_Inertial. Setting the optional third argument to true will apply CLAHE equalization to images (Mainly for TUM-VI dataset).
``` ```
rosrun ORB_SLAM3 Mono PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE [EQUALIZATION] rosrun ORB_SLAM3 Mono PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE [EQUALIZATION]
``` ```
### Running Stereo Node ### Running Stereo Node
@@ -182,9 +184,9 @@ For a stereo input from topic `/camera/left/image_raw` and `/camera/right/image_
For a stereo input from topics `/camera/left/image_raw` and `/camera/right/image_raw`, and an inertial input from topic `/imu`, run node ORB_SLAM3/Stereo_Inertial. You will need to provide the vocabulary file and a settings file, including rectification matrices if required in a similar way to Stereo case: For a stereo input from topics `/camera/left/image_raw` and `/camera/right/image_raw`, and an inertial input from topic `/imu`, run node ORB_SLAM3/Stereo_Inertial. You will need to provide the vocabulary file and a settings file, including rectification matrices if required in a similar way to Stereo case:
``` ```
rosrun ORB_SLAM3 Stereo_Inertial PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ONLINE_RECTIFICATION [EQUALIZATION] rosrun ORB_SLAM3 Stereo_Inertial PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ONLINE_RECTIFICATION [EQUALIZATION]
``` ```
### Running RGB_D Node ### Running RGB_D Node
For an RGB-D input from topics `/camera/rgb/image_raw` and `/camera/depth_registered/image_raw`, run node ORB_SLAM3/RGBD. You will need to provide the vocabulary file and a settings file. See the RGB-D example above. For an RGB-D input from topics `/camera/rgb/image_raw` and `/camera/depth_registered/image_raw`, run node ORB_SLAM3/RGBD. You will need to provide the vocabulary file and a settings file. See the RGB-D example above.
@@ -196,15 +198,15 @@ For an RGB-D input from topics `/camera/rgb/image_raw` and `/camera/depth_regist
``` ```
roscore roscore
``` ```
``` ```
rosrun ORB_SLAM3 Stereo_Inertial Vocabulary/ORBvoc.txt Examples/Stereo-Inertial/EuRoC.yaml true rosrun ORB_SLAM3 Stereo_Inertial Vocabulary/ORBvoc.txt Examples/Stereo-Inertial/EuRoC.yaml true
``` ```
``` ```
rosbag play --pause V1_02_medium.bag /cam0/image_raw:=/camera/left/image_raw /cam1/image_raw:=/camera/right/image_raw /imu0:=/imu rosbag play --pause V1_02_medium.bag /cam0/image_raw:=/camera/left/image_raw /cam1/image_raw:=/camera/right/image_raw /imu0:=/imu
``` ```
Once ORB-SLAM3 has loaded the vocabulary, press space in the rosbag tab. Once ORB-SLAM3 has loaded the vocabulary, press space in the rosbag tab.
**Remark:** For rosbags from TUM-VI dataset, some play issue may appear due to chunk size. One possible solution is to rebag them with the default chunk size, for example: **Remark:** For rosbags from TUM-VI dataset, some play issue may appear due to chunk size. One possible solution is to rebag them with the default chunk size, for example: