Design and analysis of a framework for real-time vision-based SLAM using Rao-Blackwellised particle filters

Sim, Robert B.; Elinas, Pantelis; Griffin, Melissa J.; Shyr, Alex; Little, James J.

doi:10.1109/crv.2006.25

Cited by 66 publications

(19 citation statements)

References 21 publications

(27 reference statements)

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“…Metric approaches ( [12], [17], [19], [20], [24], [26]) aim to reconstruct the spatial arrangement of map elements, in the form of landmark maps [19], occupancy grids [20], or sets of range scans [17] (please refer to [27] for a more detailed classification). Although some non-probabilistic methods have been proposed to build metric maps ( [12], [17]), the vast majority of works on metric SLAM rely on a probabilistic representation of the robot pose and the map, where Bayesian filtering estimates the corresponding probability distributions [5].…”

Section: Previous Researchmentioning

confidence: 99%

“…Simultaneous Localization and Mapping (SLAM) has being intensively studied by researchers in the last decade, leading to approaches that can be classified into three welldifferentiated paradigms depending on the underlying map structure: metric ( [5], [12], [24], [26]), topological ( [22], [23]), or hybrid representations ( [7], [15], [28]). …”

Section: Introductionmentioning

confidence: 99%

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A New Approach for Large-Scale Localization and Mapping: Hybrid Metric-Topological SLAM

Blanco

Fernández‐Madrigal

González

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Most successful works in Simultaneous Localization and Mapping (SLAM) aim to build a metric map under a probabilistic viewpoint employing Bayesian filtering techniques. This work introduces a new hybrid metrictopological approach, where the aim is to reconstruct the path of the robot in a hybrid continuous-discrete state space which naturally combines metric and topological maps. Our fundamental contributions are: (i) the estimation of the topological path, an improvement similar to that of RaoBlackwellized Particle Filters (RBPF) and FastSLAM in the field of metric map building; and (ii) the application of grounded methods to the abstraction of topology (including loop closure) from raw sensor readings. It is remarkable that our approach could be still represented as a Bayesian inference problem, becoming an extension of purely metric SLAM. Besides providing the formal definitions and the basics for our approach, we also describe a practical implementation aimed to real-time operation. Promising experimental results mapping large environments with multiple nested loops (~30.000 m 2 , ~2Km robot path) validate our work.

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Section: Previous Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach for Large-Scale Localization and Mapping: Hybrid Metric-Topological SLAM

Blanco

Fernández‐Madrigal

González

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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“…Important works in this area include those by Davidson et al (2007) who created the algorithm using single camera and called it monoSLAM. Other works are those by Sim and Elinas which are concerned with Rao-Blackwellised particle filters in the visual SLAM (Sim et al, 2005(Sim et al, , 2006(Sim et al, , 2007. The more recent works are by Morisset and Rusu (2009) with grid-based 3D mapping.…”

Section: Introductionmentioning

confidence: 96%

Path planning and traversable area marking for stereo vision-based 3D map building

Rozman

Františ

Zboř

et al. 2011

IJCVR

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This paper deals with the path-planning of a robot during the exploration and 3D map building in the unknown environment. The robot uses a stereocamera for significant features detection, then matches them in both images and computes their 3D coordinates. This way the robot incrementally creates the 3D map of its surroundings and tries to explore as much area as possible. This paper describes the way of marking the traversable path in the map created so far and the path planning to the next explored goal point. As the map uses points and triangles to represent shape of the surface, the traversable area marking and the path planning is also done in the map which is represented in the same way.

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“…Existing vision-based SLAM (Simultaneous Localization and Mapping) or SFM (Structure From Motion) algorithms expend storage and computational resources that grow super-linearly with the sequence length, and incur growing localization error over time; these methods typically handle only short-duration, short-excursion sequences [9,23].…”

Section: Introductionmentioning

confidence: 99%

Wide-Area Egomotion Estimation from Known 3D Structure

Koch

Teller

2007

2007 IEEE Conference on Computer Vision and Pattern Recognition

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Robust egomotion recovery for extended camera excursions has long been a challenge for machine vision researchers. Existing algorithms handle spatially limited environments and tend to consume prohibitive computational resources with increasing excursion time and distance.We describe an egomotion estimation algorithm that takes as input a coarse 3D model of an environment, and an omnidirectional video sequence captured within the environment, and produces as output a reconstruction of the camera's 6-DOF egomotion expressed in the coordinates of the input model. The principal novelty of our method is a robust matching algorithm that associates 2D edges from the video with 3D line segments from the input model.Our system handles 3-DOF and 6-DOF camera excursions of hundreds of meters within real, cluttered environments. It uses a novel prior visibility analysis to speed initialization and dramatically accelerate image-to-model matching. We demonstrate the method's operation, and qualitatively and quantitatively evaluate its performance, on both synthetic and real image sequences.

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Design and analysis of a framework for real-time vision-based SLAM using Rao-Blackwellised particle filters

Cited by 66 publications

References 21 publications

A New Approach for Large-Scale Localization and Mapping: Hybrid Metric-Topological SLAM

A New Approach for Large-Scale Localization and Mapping: Hybrid Metric-Topological SLAM

Path planning and traversable area marking for stereo vision-based 3D map building

Wide-Area Egomotion Estimation from Known 3D Structure

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