A comparison of maximum likelihood methods for appearance-based minimalistic SLAM

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

This paper compares the performances of several algorithms that address the problem of Simultaneous Localization and Mapping (SLAM) for the case of very small, resource-limited robots. These robots have poor odometry and can typically only carry a single monocular camera. These algorithms do not make the typical SLAM assumption that metric distance/bearing information to landmarks is available. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. The performances of a physics-inspired maximum likelihood (ML) estimator, the Iterated form of the Extended Kalman Filter (IEKF), and a batch-processed linearized ML estimator are compared under various odometric noise models.

Original languageEnglish (US)
Pages (from-to)1777-1782
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume2004
Issue number2
StatePublished - Jul 5 2004
EventProceedings- 2004 IEEE International Conference on Robotics and Automation - New Orleans, LA, United States
Duration: Apr 26 2004May 1 2004

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Maximum likelihood
Robots
Bearings (structural)
Extended Kalman filters
Physics
Cameras
Sensors

Cite this

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title = "A comparison of maximum likelihood methods for appearance-based minimalistic SLAM",
abstract = "This paper compares the performances of several algorithms that address the problem of Simultaneous Localization and Mapping (SLAM) for the case of very small, resource-limited robots. These robots have poor odometry and can typically only carry a single monocular camera. These algorithms do not make the typical SLAM assumption that metric distance/bearing information to landmarks is available. Instead, the robot registers a distinctive sensor {"}signature{"}, based on its current location, which is used to match robot positions. The performances of a physics-inspired maximum likelihood (ML) estimator, the Iterated form of the Extended Kalman Filter (IEKF), and a batch-processed linearized ML estimator are compared under various odometric noise models.",
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AU - Roumeliotis, Stergios I.

AU - Gini, Maria

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AB - This paper compares the performances of several algorithms that address the problem of Simultaneous Localization and Mapping (SLAM) for the case of very small, resource-limited robots. These robots have poor odometry and can typically only carry a single monocular camera. These algorithms do not make the typical SLAM assumption that metric distance/bearing information to landmarks is available. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. The performances of a physics-inspired maximum likelihood (ML) estimator, the Iterated form of the Extended Kalman Filter (IEKF), and a batch-processed linearized ML estimator are compared under various odometric noise models.

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