From 5ad09bf8e9c1cb4f8f9a8789ebaffb2bb8d2fc98 Mon Sep 17 00:00:00 2001 From: Raul Mur-Artal Date: Tue, 6 Jun 2017 21:46:39 -0700 Subject: [PATCH] Fix markdown issues --- README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 11a88a858b..122db22296 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ alt="ORB-SLAM2" width="240" height="180" border="10" /> alt="ORB-SLAM2" width="240" height="180" border="10" /> -###Related Publications: +### Related Publications: [Monocular] Raúl Mur-Artal, J. M. M. Montiel and Juan D. Tardós. **ORB-SLAM: A Versatile and Accurate Monocular SLAM System**. *IEEE Transactions on Robotics,* vol. 31, no. 5, pp. 1147-1163, 2015. (**2015 IEEE Transactions on Robotics Best Paper Award**). **[PDF](http://webdiis.unizar.es/~raulmur/MurMontielTardosTRO15.pdf)**. @@ -23,7 +23,7 @@ alt="ORB-SLAM2" width="240" height="180" border="10" /> [DBoW2 Place Recognizer] Dorian Gálvez-López and Juan D. Tardós. **Bags of Binary Words for Fast Place Recognition in Image Sequences**. *IEEE Transactions on Robotics,* vol. 28, no. 5, pp. 1188-1197, 2012. **[PDF](http://doriangalvez.com/php/dl.php?dlp=GalvezTRO12.pdf)** -#1. License +# 1. License ORB-SLAM2 is released under a [GPLv3 license](https://github.com/raulmur/ORB_SLAM2/blob/master/License-gpl.txt). For a list of all code/library dependencies (and associated licenses), please see [Dependencies.md](https://github.com/raulmur/ORB_SLAM2/blob/master/Dependencies.md). @@ -51,7 +51,7 @@ if you use ORB-SLAM2 (Stereo or RGB-D) in an academic work, please cite: year={2016} } -#2. Prerequisites +# 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 @@ -72,7 +72,7 @@ We use modified versions of the [DBoW2](https://github.com/dorian3d/DBoW2) libra ## 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. -#3. Building ORB-SLAM2 library and TUM/KITTI examples +# 3. Building ORB-SLAM2 library and examples Clone the repository: ``` @@ -88,7 +88,7 @@ chmod +x build.sh This will create **libORB_SLAM2.so** at *lib* folder and the executables **mono_tum**, **mono_kitti**, **rgbd_tum**, **stereo_kitti**, **mono_euroc** and **stereo_euroc** in *Examples* folder. -#4. Monocular Examples +# 4. Monocular Examples ## TUM Dataset @@ -121,7 +121,7 @@ This will create **libORB_SLAM2.so** at *lib* folder and the executables **mono ./Examples/Monocular/mono_euroc Vocabulary/ORBvoc.txt Examples/Monocular/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data Examples/Monocular/EuRoC_TimeStamps/SEQUENCE.txt ``` -#5. Stereo Examples +# 5. Stereo Examples ## KITTI Dataset @@ -144,7 +144,7 @@ This will create **libORB_SLAM2.so** at *lib* folder and the executables **mono ./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data PATH_TO_SEQUENCE/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt ``` -#6. RGB-D Example +# 6. RGB-D Example ## TUM Dataset @@ -162,7 +162,7 @@ This will create **libORB_SLAM2.so** at *lib* folder and the executables **mono ./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE ``` -#7. ROS Examples +# 7. ROS Examples ### Building the nodes for mono, monoAR, stereo and RGB-D 1. Add the path including *Examples/ROS/ORB_SLAM2* 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: @@ -222,10 +222,10 @@ For an RGB-D input from topics `/camera/rgb/image_raw` and `/camera/depth_regist rosrun ORB_SLAM2 RGBD PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ``` -#8. Processing your own sequences +# 8. Processing your own sequences You will need to create a settings file with the calibration of your camera. See the settings file provided for the TUM and KITTI datasets for monocular, stereo and RGB-D cameras. We use the calibration model of OpenCV. See the examples to learn how to create a program that makes use of the ORB-SLAM2 library and how to pass images to the SLAM system. Stereo input must be synchronized and rectified. RGB-D input must be synchronized and depth registered. -#9. SLAM and Localization Modes +# 9. SLAM and Localization Modes You can change between the *SLAM* and *Localization mode* using the GUI of the map viewer. ### SLAM Mode