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.

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1. Probabilistic Robotics by Dieter Fox, Sebastian Thrun, and Wolfram Burgard (01/09/2005)
2. Simultaneous Localization and Mapping: Exactly Sparse Information Filters by Zhan Wang, Shoudong Huang and Gamini Dissanayake(31/05/2011)
3. Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods by Juan-Antonio Fernández-Madrigal and José Luis Blanco Claraco (01/01/2012)

1. Robot Mapping - UniFreiburg by Gian Diego Tipaldi and Wolfram Burgard (2015-2016)
2. Introduction to Mobile Robotics - UniFreiburg by Wolfram Burgard, Michael Ruhnke and Bastian Steder (2015-2016)
3. Computer Vision II: Multiple View Geometry - TUM by Daniel Cremers ( Spring 2016)
4. Advanced Robotics - UCBerkeley by Pieter Abbeel (Fall 2015)

1. Mapping, Localization, and Self-Driving Vehicles By John Leonard
2. Robust and Efficient Real-time Mapping for Autonomous Robots By Michael Kaess
3. KinectFusion - Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera By David Kim

1. A Solution to the Simultaneous Localization and Map Building (SLAM) Problem

1. Simulataneous Localization and Mapping with the Extended Kalman Filter

1. Gamini Dissanayake
2. Shoudong Huang

1. Intel Research Lab (Seattle)

1. ORB-SLAM
2. LSD-SLAM
3. ORB-SLAM2
4. DVO: Dense Visual Odometry
5. SVO: Semi-Direct Monocular Visual Odometry
6. G2O: General Graph Optimization
7. RGBD-SLAM

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