2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace) 2019
DOI: 10.1109/metroaerospace.2019.8869696
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UAS for positioning and field mapping using LIDAR and IMU sensors data: Kalman filtering and integration

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Cited by 19 publications
(8 citation statements)
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“…without compensation for cross-axis sensitivity, βˆ†π‘† 𝐢_π‘Œπ‘ and βˆ†π‘† 𝐢_𝑍𝑁 refer to the wavelength change rate of the sensor owing to the cross-axis sensitivities corresponding to the non-primary y and z directions of motion, respectively, βˆ†πœ† π‘Œ and βˆ†πœ† 𝑍 refer to the wavelength change corresponding to the non-primary direction, and βˆ†πœ† 𝑋+π‘Œ+𝑍 is the summation of the wavelength change in three axis directions. The use of the Β± symbol in (11) indicates that if the wavelength shift is positive, it should be reduced by the cross-axis sensitivity, whereas if the wavelength shift is negative, it should be increased by the cross-axis sensitivity.…”
Section: Evaluation As An Acceleration Sensormentioning
confidence: 99%
See 1 more Smart Citation
“…without compensation for cross-axis sensitivity, βˆ†π‘† 𝐢_π‘Œπ‘ and βˆ†π‘† 𝐢_𝑍𝑁 refer to the wavelength change rate of the sensor owing to the cross-axis sensitivities corresponding to the non-primary y and z directions of motion, respectively, βˆ†πœ† π‘Œ and βˆ†πœ† 𝑍 refer to the wavelength change corresponding to the non-primary direction, and βˆ†πœ† 𝑋+π‘Œ+𝑍 is the summation of the wavelength change in three axis directions. The use of the Β± symbol in (11) indicates that if the wavelength shift is positive, it should be reduced by the cross-axis sensitivity, whereas if the wavelength shift is negative, it should be increased by the cross-axis sensitivity.…”
Section: Evaluation As An Acceleration Sensormentioning
confidence: 99%
“…Conventional MEMS-based IMU sensors detect and measure changes in static capacitance according to changes in acceleration or angular velocity [11], [12]. This approach is inevitably influenced by external EMI, adversely affecting the noise property (such as random walk), which is one of the main characteristics of IMU sensors [13].…”
Section: Introductionmentioning
confidence: 99%
“…Data were collected in static conditions for 120 s, at a sampling rate of 4 Hz (0.25 s sampling time). During the tests, a real-time noise-removal procedure developed by the authors [19] was performed using a simple 1-D low-pass Kalman filter (KF). The sensor characterization has been performed by measuring the distance from an obstacle in the range 30-180 cm, with 5-cm steps.…”
Section: Lidar Sensormentioning
confidence: 99%
“…This work, extending the preliminary conceptual design recently presented in a congress paper [19], deals with the development of a UAS with a Positioning, field mapping, Obstacle Detection and Avoiding (PODA) embedded system [20], exploiting lightweight, low-cost and fastresponse sensors. The chosen sensors are a 10-Degrees-of-Freedom (DoF) Inertial Measurement Unit (IMU) and a Light Detection and Ranging (LiDAR) sensor.…”
Section: Introductionmentioning
confidence: 99%
“…Much work has been published in the field of extrapolation, filtering, and fusion of IMUs and other sensor data (GPS/GNSS, LiDAR, sonar, etc.) for attitude and velocity estimation [7][8][9][10][11], and for meteorology and wind estimation for atmospheric energy harvesting [12][13][14][15][16][17]. For small, low-cost UAVs, minimization of sensing requirements and expansion of flight envelope, endurance, and mission capability are priorities.…”
Section: Introductionmentioning
confidence: 99%