SLAM相关资源大列表
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摘要
Simultaneous Localization and Mapping, also known as SLAM, is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
News
For researchers, please read the recent review paper, Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age, from Cesar Cadena, Luca Carlone et al.
Table of Contents
Books
Courses, Lectures and Workshops
Papers
Researchers
Datasets
Code
Miscellaneous
Contributing
Books
State Estimation for Robotic -- A Matrix Lie Group Approach by Timothy D. Barfoot, 2016
Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods by Juan-Antonio Fernández-Madrigal and José Luis Blanco Claraco, 2012
Simultaneous Localization and Mapping: Exactly Sparse Information Filters by Zhan Wang, Shoudong Huang and Gamini Dissanayake, 2011
Probabilistic Robotics by Dieter Fox, Sebastian Thrun, and Wolfram Burgard, 2005
An Invitation to 3-D Vision -- from Images to Geometric Models by Yi Ma, Stefano Soatto, Jana Kosecka and Shankar S. Sastry, 2005
Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman, 2004
Numerical Optimization by Jorge Nocedal and Stephen J. Wright, 1999
Courses, Lectures and Workshops
SLAM Tutorial@ICRA 2016
Geometry and Beyond - Representations, Physics, and Scene Understanding for Robotics at Robotics: Science and Systems (2016)
Robotics - UPenn on Coursera by Vijay Kumar (2016)
Robot Mapping - UniFreiburg by Gian Diego Tipaldi and Wolfram Burgard (2015-2016)
Robot Mapping - UniBonn by Cyrill Stachniss (2016)
Introduction to Mobile Robotics - UniFreiburg by Wolfram Burgard, Michael Ruhnke and Bastian Steder (2015-2016)
Computer Vision II: Multiple View Geometry - TUM by Daniel Cremers ( Spring 2016)
Advanced Robotics - UCBerkeley by Pieter Abbeel (Fall 2015)
Mapping, Localization, and Self-Driving Vehicles at CMU RI seminar by John Leonard (2015)
The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM sponsored by Australian Centre for Robotics and Vision (2015)
Robotics - UPenn by Philip Dames and Kostas Daniilidis (2014)
Autonomous Navigation for Flying Robots on EdX by Jurgen Sturm and Daniel Cremers (2014)
Robust and Efficient Real-time Mapping for Autonomous Robots at CMU RI seminar by Michael Kaess (2014)
KinectFusion - Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera by David Kim (2012)
SLAM Summer School organized by Australian Centre for Field Robotics (2009)
SLAM Summer School organized by University of Oxford and Imperial College London (2006)
SLAM Summer School organized by KTH Royal Institute of Technology (2002)
Papers
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age (2016)
Direct Sparse Odometry (2016)
Modelling Uncertainty in Deep Learning for Camera Relocalization (2016)
Large-Scale Cooperative 3D Visual-Inertial Mapping in a Manhattan World (2016)
Towards Lifelong Feature-Based Mapping in Semi-Static Environments (2016)
Tree-Connectivity: Evaluating the Graphical Structure of SLAM (2016)
Visual-Inertial Direct SLAM (2016)
A Unified Resource-Constrained Framework for Graph SLAM (2016)
Multi-Level Mapping: Real-time Dense Monocular SLAM (2016)
Lagrangian duality in 3D SLAM: Verification techniques and optimal solutions (2015)
A Solution to the Simultaneous Localization and Map Building (SLAM) Problem
Simulataneous Localization and Mapping with the Extended Kalman Filter
Researchers
United States
John Leonard
Sebastian Thrun
Frank Dellaert
Dieter Fox
Stergios I. Roumeliotis
Vijay Kumar
Ryan Eustice
Michael Kaess
Guoquan (Paul) Huang
Gabe Sibley
Luca Carlone
Andrea Censi
Europe
Paul Newman
Roland Siegwart
Juan Nieto
Wolfram Burgard
Jose Neira
Davide Scaramuzza
Australia
Cesar Cadena
Ian Reid
Tim Bailey
Gamini Dissanayake
Shoudong Huang
Datasets
Intel Research Lab (Seattle)
Code
ORB-SLAM
LSD-SLAM
ORB-SLAM2
DVO: Dense Visual Odometry
SVO: Semi-Direct Monocular Visual Odometry
G2O: General Graph Optimization
RGBD-SLAM
链接：
https://github.com/kanster/awesome-slam
原文链接：
https://m.weibo.cn/1402400261/4140930397690270
来源：爱可可-爱生活（版权归原作者及刊载媒体所有）
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编辑：郭晓非
审核：高敬凯
指导：万剑华教授(微信号wjh18266613129)