Simultaneous localization and mapping with unknown data association using FastSLAM

Montemerlo, Michael; Thrun, Sebastian

doi:10.1109/robot.2003.1241885

Cited by 466 publications

(410 citation statements)

References 13 publications

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“…For example, the spatial module we describe below would not be appropriate for nor have the accuracy and resolution of state-of-the-art simultaneous localization and mapping (SLAM) algorithms (Montemerlo & Thrun, 2003). A SLAM algorithm, however, while useful to a robot during its own localization and navigational process, is not at all useful when trying to understand why humans systematically think they are closer to landmarks than they really are.…”

Section: Act-r/embodiedmentioning

confidence: 99%

ACT-R/E: An Embodied Cognitive Architecture for Human-Robot Interaction

Trafton

Hiatt

Harrison

et al. 2013

JHRI

149

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We present ACT-R/E (Adaptive Character of Thought-Rational / Embodied), a cognitive architecture for human-robot interaction. Our reason for using ACT-R/E is two-fold. First, ACT-R/E enables researchers to build good embodied models of people to understand how and why people think the way they do. Then, we leverage that knowledge of people by using it to predict what a person will do in different situations; e.g., that a person may forget something and may need to be reminded or that a person cannot see everything the robot sees. We also discuss methods of how to evaluate a cognitive architecture and show numerous, empirically validated examples of ACT-R/E models.

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Section: Act-r/embodiedmentioning

confidence: 99%

ACT-R/E: An Embodied Cognitive Architecture for Human-Robot Interaction

Trafton

Hiatt

Harrison

et al. 2013

JHRI

149

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show abstract

“…Each filter estimates the position of the a single landmark given the current pose estimate. In this sense, our approach is similar to FastSLAM [19]. The difference to FastSLAM is that the partially initialised features x new:j are not used for state estimation immediately.…”

Section: Feature Initialisationmentioning

confidence: 99%

Scale Drift-Aware Large Scale Monocular SLAM

Strasdat¹,

Montiel²,

Davison³

2010

Robotics: Science and Systems VI

354

309

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Abstract-State of the art visual SLAM systems have recently been presented which are capable of accurate, large-scale and real-time performance, but most of these require stereo vision. Important application areas in robotics and beyond open up if similar performance can be demonstrated using monocular vision, since a single camera will always be cheaper, more compact and easier to calibrate than a multi-camera rig.With high quality estimation, a single camera moving through a static scene of course effectively provides its own stereo geometry via frames distributed over time. However, a classic issue with monocular visual SLAM is that due to the purely projective nature of a single camera, motion estimates and map structure can only be recovered up to scale. Without the known inter-camera distance of a stereo rig to serve as an anchor, the scale of locally constructed map portions and the corresponding motion estimates is therefore liable to drift over time.In this paper we describe a new near real-time visual SLAM system which adopts the continuous keyframe optimisation approach of the best current stereo systems, but accounts for the additional challenges presented by monocular input. In particular, we present a new pose-graph optimisation technique which allows for the efficient correction of rotation, translation and scale drift at loop closures. Especially, we describe the Lie group of similarity transformations and its relation to the corresponding Lie algebra. We also present in detail the system's new image processing front-end which is able accurately to track hundreds of features per frame, and a filter-based approach for feature initialisation within keyframe-based SLAM. Our approach is proven via large-scale simulation and real-world experiments where a camera completes large looped trajectories.

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“…Most of the work targeted problems for mobile robot localization [21][22][23]. In Markov localization for mobile robots, the absence of an expected measurement can be used to improve localization.…”

Section: Related Workmentioning

confidence: 99%

“…However, all these works only consider cases where an expected observation is missing. In [23] the authors have shown how negative information can be incorporated into FastSLAM, a system that is alternative to the complex Extended Kalman Filter approach for robot localization. In wireless sensor localization, Monte-Carlo localization algorithms make use of negative information [24].…”

Section: Related Workmentioning

confidence: 99%

Use of negative information in positioning and tracking algorithms

2013

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To avoid additional hardware deployment, indoor localization systems have to be designed in such a way that they rely on existing infrastructure only. Besides the processing of measurements between nodes, localization procedure can include the information of all available environment information. In order to enhance the performance of Wi-Fi based localization systems, the innovative solution presented in this paper considers also the negative information. An indoor tracking method inspired by Kalman filtering is also proposed.

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Simultaneous localization and mapping with unknown data association using FastSLAM

Cited by 466 publications

References 13 publications

ACT-R/E: An Embodied Cognitive Architecture for Human-Robot Interaction

ACT-R/E: An Embodied Cognitive Architecture for Human-Robot Interaction

Scale Drift-Aware Large Scale Monocular SLAM

Use of negative information in positioning and tracking algorithms

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