The list of vision-based SLAM / Visual Odometry open source projects, libraries, dataset, tools, and

Index

Libraries
Dataset
Tools
Projects
Learn
Miscellaneous

Libraries

Basic vision and trasformation librariesOpenCV
Eigen
Sophus
ROS
PointCloud
Thread-safe queue librariesconcurrentqueue
Intel® TBB
Facebook folly PC
Loop detectiondorian3d
Graph Optimizationceres-solver
g2o
gtasm
Vertigo
Map libraryETHZ ASL/Grip Map
OmniMapper
OctoMap

Dataset

Dataset for benchmark/test/experiment/evalutation
TUM Universtiy
KTTI Vision benchmark
UNI-Freiburg

Tools

rgbd-dataset tool from TUM
evo - evaluation tool for different trajectory formats

Projects

RGB (Monocular):PTAM
[1] Georg Klein and David Murray, "Parallel Tracking and Mapping for Small AR Workspaces", Proc. ISMAR 2007[2] Georg Klein and David Murray, "Improving the Agility of Keyframe-based SLAM", Proc. ECCV 2008
DSO. Available on ROS
Direct Sparse Odometry, J. Engel, V. Koltun, D. Cremers, In arXiv:1607.02565, 2016A Photometrically Calibrated Benchmark For Monocular Visual Odometry, J. Engel, V. Usenko, D. Cremers, In arXiv:1607.02555, 2016
LSD-SLAM. Available on ROS
LSD-SLAM: Large-Scale Direct Monocular SLAM, J. Engel, T. Schöps, D. Cremers, ECCV '14Semi-Dense Visual Odometry for a Monocular Camera, J. Engel, J. Sturm, D. Cremers, ICCV '13
ORB-SLAM. Available on ROS
[1] 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.[2] 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.
Nister's Five Point Algorithm for Essential Matrix estimation, and FAST features, with a KLT tracker
D. Nister, “An efficient solution to the five-point relative pose problem,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 26, no. 6, pp. 756–770, 2004.
SVO-SLAM. Available on ROS
Christian Forster, Matia Pizzoli, Davide Scaramuzza, "SVO: Fast Semi-direct Monocular Visual Odometry," IEEE International Conference on Robotics and Automation, 2014.
RGB and Depth (Called RGBD):OpenCV RGBD-Odometry (Visual Odometry based RGB-D images)
Real-Time Visual Odometry from Dense RGB-D Images, F. Steinbucker, J. Strum, D. Cremers, ICCV, 2011
Dense Visual SLAM for RGB-D Cameras. Available on ROS
[1]Dense Visual SLAM for RGB-D Cameras (C. Kerl, J. Sturm, D. Cremers), In Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), 2013.[2]Robust Odometry Estimation for RGB-D Cameras (C. Kerl, J. Sturm, D. Cremers), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2013[3]Real-Time Visual Odometry from Dense RGB-D Images (F. Steinbruecker, J. Sturm, D. Cremers), In Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV), 2011.
RTAB MAP - Real-Time Appearance-Based Mapping. Available on ROS
Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM, 2014Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation, 2013
ORB2-SLAM. Available on ROS
[1] 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).[2] 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.
InfiniTAM∞ v2
Kahler, O. and Prisacariu, V.~A. and Ren, C.~Y. and Sun, X. and Torr, P.~H.~S and Murray, D.~W. Very High Frame Rate Volumetric Integration of Depth Images on Mobile Device. IEEE Transactions on Visualization and Computer Graphics (Proceedings International Symposium on Mixed and Augmented Reality 2015
Kintinuous
Real-time Large Scale Dense RGB-D SLAM with Volumetric Fusion, T. Whelan, M. Kaess, H. Johannsson, M.F. Fallon, J. J. Leonard and J.B. McDonald, IJRR '14
ElasticFusion
[1] ElasticFusion: Real-Time Dense SLAM and Light Source Estimation, T. Whelan, R. F. Salas-Moreno, B. Glocker, A. J. Davison and S. Leutenegger, IJRR '16[2] ElasticFusion: Dense SLAM Without A Pose Graph, T. Whelan, S. Leutenegger, R. F. Salas-Moreno, B. Glocker and A. J. Davison, RSS '15
Co-Fusion
Martin Rünz and Lourdes Agapito. Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects. 2017 IEEE International Conference on Robotics and Automation (ICRA)
RGBD and LIDAR:Google's cartographer. Available on ROS
https://github.com/tzutalin/awesome-visual-slam