During the last years inertial navigation systems (INS) have been widespread in geodetic surveying. In particular they are recently used in several applications related to photogrammetry, such as direct geo-referencing, mobile mapping and LIDAR applications, because these sensors can determine positioning, speed and attitude of a moving platform at each acquisition time. In this framework integration of low-cost accelerometric type MEMS (Micro-Electro-Mechanical Systems) sensors with GNSS positioning systems is very interesting. In particular we present the results of some calibration tests carried out to verify the precision and reliability of such low–cost sensors MEMS, home made with reference to a “six-positions” static test, well known in literature (1998). Such tests have been carried out both in laboratory and outsite using a master and three“slave” sensors, which allow us to obtain estimates of the most relevant error-parameters (bias, scale-factor, non-orthogonality of axes).
(2010). Calibration tests on low cost inertial navigation systems . Retrieved from http://hdl.handle.net/10446/157828
Calibration tests on low cost inertial navigation systems
D'Urso, M. G.;
2010-01-01
Abstract
During the last years inertial navigation systems (INS) have been widespread in geodetic surveying. In particular they are recently used in several applications related to photogrammetry, such as direct geo-referencing, mobile mapping and LIDAR applications, because these sensors can determine positioning, speed and attitude of a moving platform at each acquisition time. In this framework integration of low-cost accelerometric type MEMS (Micro-Electro-Mechanical Systems) sensors with GNSS positioning systems is very interesting. In particular we present the results of some calibration tests carried out to verify the precision and reliability of such low–cost sensors MEMS, home made with reference to a “six-positions” static test, well known in literature (1998). Such tests have been carried out both in laboratory and outsite using a master and three“slave” sensors, which allow us to obtain estimates of the most relevant error-parameters (bias, scale-factor, non-orthogonality of axes).File | Dimensione del file | Formato | |
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