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Cited by 167

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

EKF-based 3D SLAM for structured environment reconstruction

Open access
Weingarten, J., Siegwart, R.·2005·2005 IEEE/RSJ International Conference on Intelligent Robots and Systems

Abstract

This paper presents the extension and experimental validation of the widely used EKF-based SLAM algorithm to 3D space. It uses planar features extracted probabilistically from dense three-dimensional point clouds generated by a rotating 2D laser scanner. These features are represented in compliance with the symmetries and perturbation model (SPmodel) in a stochastic map. As the robot moves, this map is updated incrementally while its pose is tracked by using an extended Kalman filter. After showing how three-dimensional data can be generated, the probabilistic feature extraction method is described, capable of robustly extracting (infinite) planes from structured environments. The SLAM algorithm is then used to track a robot moving through an indoor environment and its capabilities in terms of 3D reconstruction are analyzed.

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Metadata

Authors
Weingarten, J., & Siegwart, R.
Venue
2005 IEEE/RSJ International Conference on Intelligent Robots and Systems
Publisher
IEEE
Published
2005
Pages
3834-3839
OpenAlex
W2109341802
Cited by
167
References
1
FWCI
30.15
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: EKF-based 3D SLAM for structured environment reconstruction. https://app.cochat.ai/papers/10.1109/iros.2005.1545285
The paper
Weingarten, J., & Siegwart, R. (2005). EKF-based 3D SLAM for structured environment reconstruction. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2005.1545285

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.