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Localization and mapping algorithm based on Lidar-IMU-Camera fusion
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作者 Yibing Zhao Yuhe Liang +2 位作者 Zhenqiang Ma Lie Guo Hexin Zhang 《Journal of Intelligent and Connected Vehicles》 EI 2024年第2期97-107,共11页
Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems.In a complex traffic environment,the signal of the Global Navigation Satellite System(GNSS)will be bloc... Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems.In a complex traffic environment,the signal of the Global Navigation Satellite System(GNSS)will be blocked,leading to inaccurate vehicle positioning.To ensure the security of automatic electric campus vehicles,this study is based on the Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain(LEGO-LOAM)algorithm with a monocular vision system added.An algorithm framework based on Lidar-IMU-Camera(Lidar means light detection and ranging)fusion was proposed.A lightweight monocular vision odometer model was used,and the LEGO-LOAM system was employed to initialize monocular vision.The visual odometer information was taken as the initial value of the laser odometer.At the back-end opti9mization phase error state,the Kalman filtering fusion algorithm was employed to fuse the visual odometer and LEGO-LOAM system for positioning.The visual word bag model was applied to perform loopback detection.Taking the test results into account,the laser radar loopback detection was further optimized,reducing the accumulated positioning error.The real car experiment results showed that our algorithm could improve the mapping quality and positioning accuracy in the campus environment.The Lidar-IMU-Camera algorithm framework was verified on the Hong Kong city dataset UrbanNav.Compared with the LEGO-LOAM algorithm,the results show that the proposed algorithm can effectively reduce map drift,improve map resolution,and output more accurate driving trajectory information. 展开更多
关键词 Lightweight and Ground-Optimized Lidar Odometry and Mapping on variable terrain(LEGO-LOAM) monocular vision system error state Kalman filter ODOMETER
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Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil 被引量:1
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作者 Sérgio H.G.SILVA David C.WEINDORF +4 位作者 Wilson M.FARIA Leandro C.PINTO Michele D.MENEZES Luiz R.G.GUILHERME Nilton CURI 《Pedosphere》 SCIE CAS CSCD 2021年第4期615-626,共12页
Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging ... Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest(RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models(DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO_(2) contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions. 展开更多
关键词 magnetic susceptibility MAGNETOMETER portable X-ray fluorescence spectrometer soil class soil spatial analysis spatial resolution terrain variables
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