Error Propagation In Odometry
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ChapterRobotics Research Volume 6 of the series Springer Tracts in Advanced Robotics pp 545-558 Date: 30 June 2003General Solution for Linearized Error Propagation in Vehicle OdometryAlonzo KellyAffiliated withRobotics Institute, Carnegie Mellon University Buy this eBook * Final gross prices may vary according to local VAT. Get Access Abstract Although odometry is nonlinear, it yields sufficiently to linearized analysis to produce a closed-form transition matrix and a symbolic general solution for both deterministic and stochastic error propagation. Accordingly, error propagation in vehicle http://www.ri.cmu.edu/publication_view.html?pub_id=6154 odometry can be understood at a level of theoretical rigor equivalent to the well-known Schuler dynamics of inertial navigation. While response to initial conditions is path-independent, response to input errors can be related to path functionals. These trajectory moments are integral transforms which functions like the moment of inertia or the Laplace transform — enabling many error propagation calculations http://link.springer.com/chapter/10.1007%2F3-540-36460-9_36 to be performed by hand in closed-form. Page %P Close Plain text Look Inside Chapter Metrics Provided by Bookmetrix Reference tools Export citation EndNote (.ENW) JabRef (.BIB) Mendeley (.BIB) Papers (.RIS) Zotero (.RIS) BibTeX (.BIB) Add to Papers Other actions About this Book Reprints and Permissions Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn Supplementary Material (0) References (8) ReferencesBorenstein J, Feng L (1995) Correction of systematic odometry error in mobile robots. In Proceedings of IEEE/RSJ International Conference on Robotics and Systems (IROS 95), Pittsburgh, PA.Brogan WL (1974) Modern control theory. Quantum, New York, NY.Chong KS, Kleeman L (1997) Accurate odometry and error modelling for a mobile robot. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA 1997), Albuquerque, New Mexico.Milliken RJ, Zoller CJ (1978) Principle of operation of navstar and system characteristics. Navigation, 25(2):95–106.Nettleton EW, Gibbens PW, Durrant-Whyte HF (2000) Closed form solutions to the multiple platform simultaneous localization and map building (slam) problem. In Proceedings of AeroSense 2000, Orlando FL, USA.Pinson JC (1963) Inertial gu
Request full-text Linearized Error Propagation in OdometryArticle in The International Journal of Robotics Research 23(2):179-218 · February 2004 with 44 ReadsDOI: 10.1177/0278364904041326 · Source: DBLP1st https://www.researchgate.net/publication/220121780_Linearized_Error_Propagation_in_Odometry Alonzo Kelly26.9 · Carnegie Mellon UniversityAbstractThe related fields of mobile robotics http://ieeexplore.ieee.org/iel7/3516/6828811/06746183.pdf and ground vehicle localization lack a linearized theory of odometry error propagation. By contrast, the equivalent Schuler dynamics which apply to inertial guidance have been known and exploited for decades. In this paper, the gen- eral solution of linearized propagation dynamics of both error propagation systematic and random errors for vehicle odometry is developed and validated. The associated integral transforms are applied to the task of eliciting the major dynamic behaviors of errors for several forms of odom- etry. Interesting behaviors include path independence, response to symmetric inputs, zeros, extrema, monotonicity and conservation. Applications to systems theory, systems error propagation in design, and calibration are illustrated. KEY WORDS—AUTHOR: PLEASE PROVIDEDo you want to read the rest of this article?Request full-text CitationsCitations72ReferencesReferences24Practical Infrared Visual Odometry"3) Ground-truth: All types of odometry suffer from unbounded error and incremental drifts. In addition, a typical characteristic is that the systematic error in odometry systems grows with the distance from the starting point, but the error is frequently reduced as the vehicle loops around and returns towards the origin [32]. This indicates that driving in a loop and computing the average Euclidean distance error to the origin can be a weak measure for the performance of odometry systems. "[Show abstract] [Hide abstract] ABSTRACT: The use of cameras as a sensor for odometry estimation is an active research topic that has seen significant growth in recent years. Most methods, however, are only suitable for standard cameras that rely on reasonable lighting. An alternative to overcome low-light conditions is the use of thermal or long-wave infrared imaging.
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