Simultaneous map building and localization for an autonomous mobile robot

By John J. Leonard, H. F. Durrant-Whyte. 1991

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Citations in the corpus

Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation (2017, background): “While simultaneous localization and mapping (SLAM) [11] and planning approaches have had difficulties with the size, weight, and power (SWaP) constraints of mobile robots [12], recent work has demonstrated these algorithms can be run on SWaP constrained platforms, such as quadrotors [3].”
The SPmap: a probabilistic framework for simultaneous localization and map building (1999, methodology): “Later, this concept was used in the works of Moutarlier et al. [3], Leonard et al. [4]–[6], and Hébert et al. [7].”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “the simultaneous estimation of the state of a vehicle and the construction of a map of the environment [65], [66], [67], [68].”
Simultaneous localization and mapping with detection and tracking of moving objects (2002, methodology): “Simultaneous localization and mapping (SLAM) as first proposed by Leonard and Durrant-Whyte [16] is to simultaneously estimate positions of newly perceived landmarks and the position of the mobile robot itself while mapping.”
Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization (2001, methodology): “All robots that do not use preplaced landmarks or GPS must employ a localization algorithm while mapping an unknown space, hence the term simultaneous localization and mapping, first coined by Leonard and Durrant-Whyte [16], [25].”
Visual Navigation for Mobile Robots: A Survey (2008, methodology): “This proposal maps image features instead of performing a geometrical representation of the environment, operating and managing a framework presented in [121] and adapting an extended Kalman filter localization framework described in [125] and [74].”
Unmanned aerial systems for photogrammetry and remote sensing: A review (2014, methodology): “, the inclusion of tie features measurements in the navigation filter and the implicit or explicit use of the corresponding images’ exterior orientation—we mention the related pioneering work in Grün (1985) on real-time bundle adjustment and the Simultaneous Localization and Mapping (SLAM) concept o”
An atlas of physical human-robot interaction (2008, unknown): “Taking as an example the problem of simultaneous localization and mapping (SLAM) [44] for a mobile service robot, the input from diﬀerent sensors (laser scan, ultrasonic, vision) may be combined in order to drive prediction algorithms.”
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing (2006, methodology): “This is similar to the extended Kalman filter approach to SLAM as pioneered by Smith et al. (1987, 1990) and Leonard and Durrant-Whyte (1991), but allows for iterating multiple times to convergence while controlling in which region one is willing to trust the linear assumption (hence, these methods…”
Simultaneous Localisation and Mapping ( SLAM ) : Part I The Essential Algorithms (2006, background): “This paper showed that as a mobile robot moves through an unknown environment taking relative observations of landmarks, the estimates of these landmarks are all necessarily correlated with each other because of the common error in estimated vehicle location [27].”
Trajectory Optimization using Reinforcement Learning for Map Exploration (2008, methodology): “Without knowledge of the robot pose, the joint distribution of the map and robot poses must be represented, a formulation known as the SLAM problem (Smith et al. 1990; Moutarlier and Chatila 1989; Leonard and Durrant-Whyte 1991).”
Simultaneous localization and mapping: part I (2006, background): “This paper showed that as a mobile robot moves through an unknown environment taking relative observations of landmarks, the estimates of these landmarks are all necessarily correlated with each other because of the common error in estimated vehicle location [ 27 ].”
Robotics-Based Location Sensing Using Wireless Ethernet (2002, methodology): “Another area of robotics where this work might be useful is SLAM (Simultaneous Localization and Mapping) [7, 33 ].”
RNN-based Learning of Compact Maps for Efficient Robot Localization (2007, methodology): “Traditional approaches to Simultaneous Localization and Mapping (SLAM) with distance sensor data are typically based on occupancy grids — that is, the robot builds an explicit map of its environment as it moves around [1, 2, 3, 4].”
Simultaneous Localization, Mapping and Moving Object Tracking (2007, methodology): “On the other hand, research on mobile robot navigation has produced four major paradigms for environment representation: feature-based approaches (Leonard and Durrant-Whyte, 1991), grid-based approaches (Elfes, 1988 Thrun et al., 1998), direct approaches (Lu and Milios, 1994, 1997), and topological…”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, unknown): “Initiated by Smith and Cheeseman [1] in 1986, the SLAM topic got popular during the 1990s with many structuring works like [2] or [3].”