Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework

By M. Bosse, Paul M. Newman, John J. Leonard, Seth J. Teller. Int. J. Robotics Res., 2004

351 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/0d8316c5efb64268f5e1b1a29e06e4d21885fd2a

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Citations in the corpus

iSAM: Incremental Smoothing and Mapping (2008, methodology): “A number of improved and numerically more stable algorithms have since been developed, based on well-known iterative techniques such as relaxation [10], [35], [36], gradient descent [37], [38], conjugate gradient [39], and, more recently, multilevel relaxation [40], [41].”
Simultaneous localization and mapping: part I (2006, background): “They include indoor [3], [4], [7], [12], outdoor [19], [21], aerial [25], and subsea [18], [36], [45].”
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures (2006, methodology): “The information filter perspective is also related to early work by Bulata and Devy (1996), whose approach acquired landmark models first in local landmark-centric reference frames, and only later assembles a consistent global map by resolving the relative information between landmarks. Another onli”
A Review of Point Cloud Registration Algorithms for Mobile Robotics (2015, methodology): “With the arrival of more standard sensors, researchers in robotics pushed the 2D registration algorithms so they could deal with larger environments with faster platforms [Bosse et al., 2004] and 3D slowly came back [Nüchter et al., 2004].”
A framework for learning semantic maps from grounded natural language descriptions (2014, background): “As with most pose graph approaches to SLAM, our algorithm may add regions that duplicate the same part of the environment, building maps that grow with time rather than space.”
Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps (2018, background): “Many other related works in the SLAM field associate one submap with each edge in the topological graph and focus on solving the relative transformation between the sub-graphs [6] [7].”
Visual Place Recognition: A Survey (2016, background): “Although it is possible to have a globally metric map, such maps are only feasible in small geographical areas, and there are mechanisms for fusing topometric maps into globally metric maps [140].”
Robust vehicle localization in urban environments using probabilistic maps (2010, methodology): “In this paper we present a new method of map-based driving that extends previous work by considering maps as probability distributions over environment properties rather than as fixed representations of the environment at a snapshot in time.”
Robust reconstruction of indoor scenes (2015, background): “Fragments are analogous to submaps, which are used in a number of robotic mapping systems [25, 5, 54, 10].”
The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection (2009, methodology): “[11–13] use the connectivity graph between submaps created by the Atlas SLAM framework [14] as the ground truth.”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “The corresponding approaches are often referred to as submapping algorithms, an idea that dates back to the initial attempts to tackle large-scale maps [19].”