A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping

By Sebastian Thrun, Wolfram Burgard, Dieter Fox. Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), 2000

893 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e6b7d59853e5a3c4cd78899ca820ada700adbd92

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Vision-based global localization and mapping for mobile robots (2005, methodology): “Thrun et al. [29] developed the museum tour-guide robot MINERVA that employs EM to learn its map and Markov localization with camera mosaics of the ceiling in addition to the laser scan occupancy map.”
Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing (2006, methodology): “[25] present an approach that employs two 2d range scanners for constructing volumetric maps.”
Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks (2002, methodology): “Thrun et al. (2000) proposed a real-time algorithm combining the strengths of EM algorithms and incremental algorithms .”
Simultaneous Localization, Mapping and Moving Object Tracking (2007, methodology): “Additional computation power is needed to run consistent pose estimation algorithms such as (Lu and Milios, 1997; Kelly and Unnikrishnan, 2003) and the previous raw scans have to be used to generate a new global consistent map (Gutmann and Konolige, 1999; Thrun et al., 2000).”
A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots (2001, background): “Our approach, which was originally described in Thrun, Burgard, and Fox (2000), is similar to recent work by Gutmann and Konolige (2000).”
An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements (2003, background): “This differs from work in [20], where only a single map is retained.”
Data association in stochastic mapping using the joint compatibility test (2001, background): “Alternative approaches search for the best solution in the vehicle pose space rather than in the correspondence space [13], [14], [15].”
RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments (2010, methodology): “For 3D laser data, the iterated closest point (ICP) algorithm and variants thereof are popular techniques [2, 30, 19, 26].”
Simultaneous localization and mapping: part I (2006, methodology): “) This approach was influenced by earlier probabilistic mapping experiments of Murphy [34] and Thrun [41].”
Real-time correlative scan matching (2009, methodology): “We follow the approach of previous approaches [18], [19] by pre-computing a lookup table containing log probabilities of lidar observation at each ( x, y ) position in the world.”
Learning compact 3D models of indoor and outdoor environments with a mobile robot (2003, methodology): “Two related papers [10; 20] discuss the use of probabilistic posterior estimation techniques to build consis tent maps in cyclic environments, where our incremental scan matching approach would be doomed to fail.”
An Incremental Self-Deployment Algorithm for Mobile Sensor Networks (2002, background): “Multi-robotexplorationandmap-buildinghasalsobeen explored by a number of authors [4, 17, 20, 19, 3, 14] who usea varietyof techniquesranging from topological matching[4] to fuzzy inference [14] andparticlefilters [21].”
Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas (2003, positive): “The map updating in our system is similar to the approach presented in [18].”
Coordination for Multi-Robot Exploration and Mapping (2000, background): “At the core of our approach is a distributed algorithm for concurrent mapping and localization in real-time [19].”