SLAM With Joint Sensor Bias Estimation: Closed Form Solutions on Observability, Error Bounds and Convergence Rates

Autor:	L.D.L. Perera, Wijerupage Sardha Wijesoma, Martin Adams
Rok vydání:	2010
Předmět:	Computer Science::Robotics Control and Systems Engineering Control theory Estimation theory Computer science Convergence (routing) System identification Word error rate State space Observability Electrical and Electronic Engineering Simultaneous localization and mapping Inertial navigation system
Zdroj:	IEEE Transactions on Control Systems Technology. 18:732-740
ISSN:	1558-0865 1063-6536
DOI:	10.1109/tcst.2009.2026165
Popis:	Notable problems in Simultaneous Localization and Mapping (SLAM) are caused by biases and drifts in both exteroceptive and proprioceptive sensors. The impacts of sensor biases include inconsistent map estimates and inaccurate localization. Unlike Map Aided Localisation with Joint Sensor Bias Estimation (MAL-JSBE), SLAM with Joint Sensor Bias Estimation (SLAM-JSBE) is more complex as it encompasses a state space, which increases with the discovery of new landmarks and the inherent map to vehicle correlations. The properties such as observability, error bounds and convergence rates of SLAM-JSBE using an augmented estimation theoretic, state space approach, are investigated here. SLAM-JSBE experiments, which adhere to the derived constraints, are demonstrated using a low cost inertial navigation sensor suite.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::595b7c7667e6c7f7b104d4116bfe7a7f https://doi.org/10.1109/tcst.2009.2026165 Zobrazit plný text záznamu