Simultaneous localization and mapping
Related papers: 20
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Simultaneous localization and mapping (SLAM) is a computational technique that enables a robot or autonomous system to build a map of an unknown environment while simultaneously tracking its own position within that map. Because neither the map nor the robot's location is known in advance, the two problems must be solved together in a mutually dependent, iterative process. In robotics and AI, SLAM is implemented using sensor data from cameras, lidar, radar, IMUs, or combinations thereof. Algorithms ranging from Extended Kalman Filters and particle filters to graph-based optimization frameworks process incoming measurements to maintain probabilistic estimates of both the robot's pose and environmental landmarks or occupancy grids. Modern variants such as visual SLAM, lidar-inertial odometry, and semantic SLAM extend these foundations to handle dynamic scenes, large-scale environments, and richer scene representations. SLAM is foundational to autonomous navigation because it removes dependence on pre-built maps or external positioning systems like GPS. It underpins self-driving vehicles, delivery robots, search-and-rescue drones, and household assistants, making it one of the most studied and practically consequential problems in mobile robotics.
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Yin Zhou, Oncel Tuzel
Citations: 4542 • 2018
Parallel Tracking and Mapping for Small AR Workspaces
Georg Klein, David W. Murray
Citations: 4244 • 2007
Simultaneous localization and mapping: part I
Hugh Durrant‐Whyte, T. Bailey
Citations: 4107 • 2006
A benchmark for the evaluation of RGB-D SLAM systems
Jrgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, Daniel Cremers
Citations: 3918 • 2012
MonoSLAM: Real-Time Single Camera SLAM
Andrew J. Davison, Ian Reid, Nicholas Molton, Olivier Stasse
Citations: 3909 • 2007
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Citations: 3257 • 2016
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
Giorgio Grisetti, Cyrill Stachniss, Wolfram Burgard
Citations: 2412 • 2007
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit
Citations: 2049 • 2002
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
Tixiao Shan, Brendan Englot, Drew Meyers, Wei Wang, Carlo Ratti, Daniela Rus
Citations: 1955 • 2020
Real-time simultaneous localisation and mapping with a single camera
Davison
Citations: 1718 • 2003
Keyframe-based visual–inertial odometry using nonlinear optimization
Stefan Leutenegger, Simon Lynen, Michael Bosse, Roland Siegwart, Paul Furgale
Citations: 1697 • 2014
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
Mark Cummins, Paul Newman
Citations: 1475 • 2008
A Tutorial on Graph-Based SLAM
Giorgio Grisetti, Rainer Kümmerle, Cyrill Stachniss, Wolfram Burgard
Citations: 1300 • 2010
Robotic mapping: a survey
Sebastian Thrun
Citations: 1293 • 2003
Mobile robot localization by tracking geometric beacons
John J. Leonard, Hugh Durrant‐Whyte
Citations: 1227 • 1991
RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
Peter Henry, Michael Krainin, Evan Herbst, Xiaofeng Ren, Dieter Fox
Citations: 1170 • 2012
DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes
Citations: 1152 • 2018
A flexible and scalable SLAM system with full 3D motion estimation
Stefan Kohlbrecher, Oskar von Stryk, Johannes Meyer, Uwe Klingauf
Citations: 1083 • 2011
DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
Chao Yu, Zuxin Liu, Xin-Jun Liu, Fugui Xie, Yi Yang, Qi Wei
Citations: 1052 • 2018
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
Berta Bescos, José M. Fácil, Javier Civera, José Neira
Citations: 924 • 2018