An RFS ‘Brute force’ formulation for Bayesian SLAM
The feature-based (FB) SLAM scenario is a vehicle moving through an environment represented by an unknown number of features. The classical problem definition is one of “a state estimation problem involving a variable number of dimensions” [28]. The SLAM problem requires a robot to navigate in an un...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/60397 |
| _version_ | 1848760603005419520 |
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| author | Mullane, J. Vo, Ba-Ngu Adams, M. Vo, B. |
| author_facet | Mullane, J. Vo, Ba-Ngu Adams, M. Vo, B. |
| author_sort | Mullane, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The feature-based (FB) SLAM scenario is a vehicle moving through an environment represented by an unknown number of features. The classical problem definition is one of “a state estimation problem involving a variable number of dimensions” [28]. The SLAM problem requires a robot to navigate in an unknown environment and use its suite of on board sensors to both construct a map and localise itself within that map without the use of any a priori information. Often, in the planar navigation context, a vehicle is assumed to acquire measurements of its surrounding environment using on board range-bearing measuring sensors. This requires joint estimates of the three dimensional robot pose (Cartesian x and y coordinates, as well as the heading angle ?), the number of features in the map as well as their two dimensional Euclidean coordinates. For a real world application, this should be performed incrementally as the robot manoeuvres about the environment. As the robot motion introduces error, coupled with a feature sensing error, both localisation and mapping must be performed simultaneously [8]. As mentioned in Chapter 2, for any given sensor, an FB decision is subject to detection and data association uncertainty, spurious measurements and measurement noise, as well as bias. |
| first_indexed | 2025-11-14T10:18:24Z |
| format | Book Chapter |
| id | curtin-20.500.11937-60397 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:18:24Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-603972018-06-18T01:01:49Z An RFS ‘Brute force’ formulation for Bayesian SLAM Mullane, J. Vo, Ba-Ngu Adams, M. Vo, B. The feature-based (FB) SLAM scenario is a vehicle moving through an environment represented by an unknown number of features. The classical problem definition is one of “a state estimation problem involving a variable number of dimensions” [28]. The SLAM problem requires a robot to navigate in an unknown environment and use its suite of on board sensors to both construct a map and localise itself within that map without the use of any a priori information. Often, in the planar navigation context, a vehicle is assumed to acquire measurements of its surrounding environment using on board range-bearing measuring sensors. This requires joint estimates of the three dimensional robot pose (Cartesian x and y coordinates, as well as the heading angle ?), the number of features in the map as well as their two dimensional Euclidean coordinates. For a real world application, this should be performed incrementally as the robot manoeuvres about the environment. As the robot motion introduces error, coupled with a feature sensing error, both localisation and mapping must be performed simultaneously [8]. As mentioned in Chapter 2, for any given sensor, an FB decision is subject to detection and data association uncertainty, spurious measurements and measurement noise, as well as bias. 2011 Book Chapter http://hdl.handle.net/20.500.11937/60397 10.1007/978-3-642-21390-8_5 restricted |
| spellingShingle | Mullane, J. Vo, Ba-Ngu Adams, M. Vo, B. An RFS ‘Brute force’ formulation for Bayesian SLAM |
| title | An RFS ‘Brute force’ formulation for Bayesian SLAM |
| title_full | An RFS ‘Brute force’ formulation for Bayesian SLAM |
| title_fullStr | An RFS ‘Brute force’ formulation for Bayesian SLAM |
| title_full_unstemmed | An RFS ‘Brute force’ formulation for Bayesian SLAM |
| title_short | An RFS ‘Brute force’ formulation for Bayesian SLAM |
| title_sort | rfs ‘brute force’ formulation for bayesian slam |
| url | http://hdl.handle.net/20.500.11937/60397 |