Personal page of SLAM Resources to follow up current SLAM trends and papers.
Inspired by Event-based vision resources
Also reference pages are listed on Pages collect resources for SLAM
What I cannot create, I do not understand. - richard feynman
Do the simplest thing that could possibly work
- Algorithms
- Sensor Model
- Datasets and Simulators
- Calibration
- Evaluation
- Workshops&Tutorials
- Survey
- Papers
- Deep Learning Related SLAM
- Semantic SLAM - Object level SLAM
- Books
- Pages : collect resources for SLAM
- Toolkit
- Videos, Lectures
- Visualization
- Image_Undistorter
- Camera Models - modified version of CamOdoCal
- ROS Image Proc => Wiki Documentation of ROS image pipeline
- GML: C++ Calibration Toolbox
- ROS camera calibration
- Camera Calibration Toolbox for Matlab
- CamOdoCal
- OCamCalib: Omni-Camera Calibration
- IMUSensorModels-Data_Analysis_Tools
- Kalibr_allan
- NaveGO: an open-source MATLAB/GNU Octave toolbox for processing INS and performing IMU analysis
- imu_utils : ROS package tool to analyze the IMU performance
- Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
- Keyframe-based monocular SLAM: design, survey, and future directions
- Local Invariant Feature Detectors: A Survey
- Visual Odometry Part I: The First 30 Years and Fundamentals
- Visual odometry: Part II: Matching, robustness, optimization, and applications
- Visual simultaneous localization and mapping : a survey
- Simultaneous Localization and mapping : a survey of current trends in Autonomous Driving
- Visual SLAM Algorithms : a survey from 2010 to 2016
- Visual Odometry, Nister, CVPR 04
- Scalable monocular SLAM, E. Eade,T. Drummond, CVPR 06
- Parallel Tracking and Mapping(PTAM) for Small AR Workspaces, Georg Klein, David Murray, ISMAR 07
- MonoSLAM, AJ Davison, Reid, Molton, Stasse, PAMI 07
- Accurate Quadrifocal Tracking for Robust 3D Visual Odometry, IEEE RA-L 07, A.I. Comport, E. Malis and P. Rives
- DTAM: Dense Tracking and Mapping in Real-Time, RA Newcombe, Steven J. Lovegrove, AJ Davison, ICCV 11
- Dense Visual SLAM for RGB-D Camera
- Semi-Dense Visual Odometry, J. Engel, J. Sturm, AJ Davision, ICCV 13
- SVO: Fast Semi-Direct Monocular Visual Odometry, C Forster, M. Pizzoli, D. Scarammuzza, ICRA 14
- LSD-SLAM: Large-Scale Direct Monocular SLAM, J. Engel, T.Schoeps, AJ Davision, ECCV 14
- REMODE, M. Pizzoli, C. Forster, D. Scrammuza, ICRA 14
- Dense Visual-Inertial Odometry for Tracking of Aggressive Motions
- ORB_SLAM, R. Mur-Artal, J. Montiel, JD Tardós, IEEE TRO 15
- OKVIS, S. Leutenegger, S. Lynen, M. Bosse, R. Siegwart, P.Furgale, IJRR 15
- DPPTAM, Concha, Alejo and Civera, Javier, IROS 15
- SOFT2 : Stereo odometry based on careful feature selection and tracking, I Cvišić, I Petrović, ECCV 15
- EVO: A Geometric Approach to Event-Based 6-DOF Parallal Tracking and Mapping in Real-time, H. Rebecq, T. Horstschaefer, G. Gallego, D. Scaramuzza, IEEE RA-L 16
- On-Manifold Preintegration for Real-Time VIO, C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza, IEEE RA-L 17
- ORB_SLAM2, R Mur-Artal, JD Tardós, IEEE TRO 17
- Direct Sparse Odometry, J. Engel, V. Kltun, AJ Davison, PAMI 17
- Real-time VIO for Event Cameras using Keyframe-based Nonlinear Optimization, H.Rebecq, T. Horstschaefer, D. Scaramuzza, BMVC 17
- ElasticFusion: Dense SLAM Without A Pose Graph
- Dense RGB-D-Inertial SLAM with Map Deformations
- SVO for Monocular and Multi-Camera Systems, C. Forster, Z. Zhang, M. Gassner, M. Werlberger, D. Scaramuzza, IEEE TRO 17
- VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator, T. Qin, Tong and Li, Peiliang, Shen, Shaojie, IEEE TRO 18
- Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios, T. Rosinol Vidal, H.Rebecq, T. Horstschaefer, D. Scaramuzza, IEEE RA-L 18
- Event-based, 6-DOF Camera Tracking from Photometric Depth Maps, Gallego, Jon E. A. Lund, E. Mueggler, H.Rebecq, T. Delbruck, D. Scaramuzza, PAMI 18
- [Loosely-Coupled Semi-Direct Monocular SLAM, Seong Hun Lee and Javier Civera, IEEE Robotics and Automation Letters] (https://arxiv.org/pdf/1807.10073.pdf)
Deep SLAM : Depth Estimation, Pose Estimation, Feature Matching, Backend etc... What ever use Deep Neural Network
- DeepVO: A Deep Learning approach for Monocular Visual Odometry, Vikram Mohanty, Shubh Agrawal, Shaswat Datta, Arna Ghosh, Vishnu D. Sharma, Debashish Chakravarty
- CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction, CVPR, 2017, Keisuke Tateno, Federico Tombari, Iro Laina, Nassir Navab
- Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry, Nan Yang, Rui Wang, J¨org St¨uckler, Daniel Cremers
- UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
- SfMLearner++: Learning Monocular Depth & Ego-Motion using Meaningful Geometric Constraints, Vignesh Prasad, Brojeshwar Bhowmick
- CNN-SVO: Improving the Mapping in Semi-Direct Visual OdometryUsing Single-Image Depth Prediction, Shing Yan Loo, Ali Jahan, Amiri, Syamsiah Mashohor, Sai Hong Tang and Hong Zhang1
- Learning monocular visual odometry with dense 3D mapping from dense 3D flow, Cheng Zhao, Li Sun, Pulak Purkait, Tom Duckett and Rustam Stolkin1
- Learning to Prevent Monocular SLAM Failure using Reinforcement Learning, Vignesh Prasad, Karmesh Yadav, Rohitashva Singh Saurabh, Swapnil Daga, Nahas Pareekutty, K. Madhava Krishna. Balaraman Ravindran, Brojeshwar Bhowmick
- CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM, Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, Andrew J. Davison.
- LS-Net: Learning to Solve Nonlinear Least Squares for Monocular Stereo. ECCV, 2018, Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison.
- DeepTAM: Deep Tracking and Mapping, Huizhong Zhou, Benjamin Ummenhofer, Thomas Brox
- Deep Auxiliary Learning for Visual Localization and Odometry, Abhinav Valada, Noha Radwan, Wolfram Burgard
- Mask-SLAM: Robust feature-based monocular SLAM by masking using semantic segmentation, CVPR 2018, Masaya Kaneko Kazuya Iwami Toru Ogawa Toshihiko Yamasaki Kiyoharu Aiza
- MagicVO: End-to-End Monocular Visual Odometry through Deep Bi-directional Recurrent Convolutional Neural Network, Jian Jiao, Jichao Jiao, Yaokai Mo, Weilun Liu, Zhongliang Deng
- Global Pose Estimation with an Attention-based Recurrent Network
- Geometric Consistency for Self-Supervised End-to-End Visual Odometry, CVPR 2018, Ganesh Iyer, J. Krishna Murthy, Gunshi Gupta1, K. Madhava Krishna1, Liam Paull
- DepthNet: A Recurrent Neural Network Architecture for Monocular Depth Prediction, CVPR 2018, Arun CS Kumar Suchendra M. Bhandarkar, Mukta Prasad
- DeepFusion: Real-Time Dense 3D Reconstruction for Monocular SLAM using Single-View Depth and Gradient Predictions. ICRA, 2019, Tristan Laidlow, Jan Czarnowski, Stefan Leutenegger.
- KO-Fusion: Dense Visual SLAM with Tightly-Coupled Kinematic and Odometric Tracking. ICRA, 2019, Charlie Houseago, Michael Bloesch, Stefan Leutenegger.
- DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features, Rong Kang, Xueming Li, Yang Liu, Xiao Liu, Jieqi Shi
- Probabilistic Data Association for Semantic SLAM, Sean L. Bowman Nikolay Atanasov Kostas Daniilidis George J. Pappas
- Fusion++: Volumetric Object-Level SLAM. 3DV, 2018, John McCormac, Ronald Clark, Michael Bloesch, Stefan Leutenegger, Andrew J. Davison.
- DynSLAM: Simultaneous Localization and Mapping in Dynamic Environments,Ioan Andrei Brsan and Peidong Liu and Marc Pollefeys and Andreas Geiger
- Python package for evaluation of odometry and SLAM
- uzh-rpg : rpg_trajectory_evaluation, papers
- TUM, useful tools for the RGBD benchmark
- TUM, Matlab tools for evaluation, provided by TUM, DSO : Direct Sparse Odometry
- Awesome SLAM Dataset :
- 2018 TUM Visual Inertial Dataset : Stereo,IMU,Calibrated(+Photometric)
- 2018 MVSEC : Multi Vehicle Stereo Event Dataset : Stereo, Event, IMU
- 2016 TUM Mono Dataset : Mono,IMU,Photometric Calibration
- 2016 RPG Event Dataset : Mono,Event,IMU
- 2016 EuRoC Dataset : Stereo,IMU
- 2015 TUM Omni Dataset : Mono,Omni,IMU
- 2014 ICL-NUIM Dataset : Mono,RGB-D
- 2014 MRPT-MALAGA Dataset
- 2013 KITTI Dataset
- 2014 CVPR Workshop and Tutorials
- 2015 ICCV Imperial college Workshop
- 2016 ICRA SLAM Tutorials
- 2017 CVPR Tutorials - pages removed
- 2018 ECCV Visual Localization Workshop
- 2018 ECCV Workshop
- 2018 IROS Workshop - Unconventional Sensing and Processing for Robotic Visual Perception, No Material..
- 2018 ECCV 3D Reconstruction meets Semantics
- 2018 CVPR Tutorials - First Deep SLAM Workshop
- 2018: http://cvpr2018.thecvf.com/program/tutorials
- 2017: http://cvpr2017.thecvf.com/program/tutorials
- 2016: http://cvpr2016.thecvf.com/program/tutorials
- 2015: http://www.pamitc.org/cvpr15/tutorials.php
- 2014: http://www.pamitc.org/cvpr14/tutorials.php
- 2013: http://www.pamitc.org/cvpr13/tutorials.php