To achieve accurate positioning of autonomous underwater vehicles, an appropriate underwater terrain database storage format for underwater terrain-matching positioning is established using multi-beam data as underwat...To achieve accurate positioning of autonomous underwater vehicles, an appropriate underwater terrain database storage format for underwater terrain-matching positioning is established using multi-beam data as underwater terrainmatching data. An underwater terrain interpolation error compensation method based on fractional Brownian motion is proposed for defects of normal terrain interpolation, and an underwater terrain-matching positioning method based on least squares estimation（LSE） is proposed for correlation analysis of topographic features. The Fisher method is introduced as a secondary criterion for pseudo localization appearing in a topographic features flat area, effectively reducing the impact of pseudo positioning points on matching accuracy and improving the positioning accuracy of terrain flat areas. Simulation experiments based on electronic chart and multi-beam sea trial data show that drift errors of an inertial navigation system can be corrected effectively using the proposed method. The positioning accuracy and practicality are high, satisfying the requirement of underwater accurate positioning.展开更多
The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed withi...The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed within a 1.5-year period, resulting in a spatially and temporally homogeneous coverage to contain the entire Amazon Basin from the Atlantic to the Pacific; Central America up to the Yucatan Peninsular in Mexico; equatorial Africa from Madagascar and Kenya in the east to Sierra Leone in the west; and Southeast Asia, including Papua New Guinea. To some extent, GRFM project is an international endeavor led by NASDA, with the goal of producing spatially and temporally contiguous Synthetic Aperture Radar (SAR) data sets over the tropical belt on the Earth by use of the JERS-1 L-band SAR, through the generation of semi-continental, 100m resolution, image mosaics. The GRFM project relies on extensive collaboration with the National Aeronautics and Space Administration (NASA), the Joint Research Center of the European Commission (JRC) and the Japanese Ministry of International Trade and Industry (MITI) for data acquisition, processing, validation and product generation. A science program is underway in parallel with product generation. This involves the agencies mentioned above, as well as a large number of international organizations, universities and individuals to perform field activities and data analysis at different levels.展开更多
At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method o...At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.展开更多
A method of underwater simultaneous localization and mapping(SLAM)based on on-board looking forward sonar is proposed.The real-time data flow is obtained to form the underwater acoustic images and these images are pre...A method of underwater simultaneous localization and mapping(SLAM)based on on-board looking forward sonar is proposed.The real-time data flow is obtained to form the underwater acoustic images and these images are pre-processed and positions of objects are extracted for SLAM.Extended Kalman filter(EKF)is selected as the kernel approach to enable the underwater vehicle to construct a feature map,and the EKF can locate the underwater vehicle through the map.In order to improve the association effciency,a novel association method based on ant colony algorithm is introduced.Results obtained on simulation data and real acoustic vision data in tank are displayed and discussed.The proposed method maintains better association effciency and reduces navigation error,and is effective and feasible.展开更多
起源于移动机器人定位的同步构图定位(simultaneous localization and mapping,SLAM)算法可以克服基于先验图的水下导航算法的不足,实现水下载体的自主导航。本文给出了基于SLAM算法的水下导航算法的系统构建和执行过程,建立了状态方程...起源于移动机器人定位的同步构图定位(simultaneous localization and mapping,SLAM)算法可以克服基于先验图的水下导航算法的不足,实现水下载体的自主导航。本文给出了基于SLAM算法的水下导航算法的系统构建和执行过程,建立了状态方程和观测方程,讨论了新特征加入的问题,并利用模拟数据进行了仿真。仿真结果表明,无论是采用航渡还是区域探索航行方式,该导航算法的定位精度均高于单纯推位方法,具有广阔的应用前景。展开更多
基金financially supported by the National Natural Science Foundation of China(Grant Nos.51179035 and 51279221)the Natural Science Foundation of Heilongjiang Province(Grant No.E201121)
文摘To achieve accurate positioning of autonomous underwater vehicles, an appropriate underwater terrain database storage format for underwater terrain-matching positioning is established using multi-beam data as underwater terrainmatching data. An underwater terrain interpolation error compensation method based on fractional Brownian motion is proposed for defects of normal terrain interpolation, and an underwater terrain-matching positioning method based on least squares estimation（LSE） is proposed for correlation analysis of topographic features. The Fisher method is introduced as a secondary criterion for pseudo localization appearing in a topographic features flat area, effectively reducing the impact of pseudo positioning points on matching accuracy and improving the positioning accuracy of terrain flat areas. Simulation experiments based on electronic chart and multi-beam sea trial data show that drift errors of an inertial navigation system can be corrected effectively using the proposed method. The positioning accuracy and practicality are high, satisfying the requirement of underwater accurate positioning.
基金Knowledge Innovation Project of CAS,No. KZCX02-308
文摘The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed within a 1.5-year period, resulting in a spatially and temporally homogeneous coverage to contain the entire Amazon Basin from the Atlantic to the Pacific; Central America up to the Yucatan Peninsular in Mexico; equatorial Africa from Madagascar and Kenya in the east to Sierra Leone in the west; and Southeast Asia, including Papua New Guinea. To some extent, GRFM project is an international endeavor led by NASDA, with the goal of producing spatially and temporally contiguous Synthetic Aperture Radar (SAR) data sets over the tropical belt on the Earth by use of the JERS-1 L-band SAR, through the generation of semi-continental, 100m resolution, image mosaics. The GRFM project relies on extensive collaboration with the National Aeronautics and Space Administration (NASA), the Joint Research Center of the European Commission (JRC) and the Japanese Ministry of International Trade and Industry (MITI) for data acquisition, processing, validation and product generation. A science program is underway in parallel with product generation. This involves the agencies mentioned above, as well as a large number of international organizations, universities and individuals to perform field activities and data analysis at different levels.
基金supported by the National Science Fund of China under Grants 61603034China Postdoctoral Science Foundation under Grant 2019M653870XB+1 种基金Beijing Municipal Natural Science Foundation (3182027)Fundamental Research Funds for the Central Universities,China,FRF-GF-17-B44,and XJS191315
文摘At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.
基金the National Natural Science Foundation of China(No.51009040)the Fund of National Defence Key Laboratory of Autonomous Underwater Vehicle Technology(No.2008002)the Scientific Service Special Fund of University in China(No.E091002)
文摘A method of underwater simultaneous localization and mapping(SLAM)based on on-board looking forward sonar is proposed.The real-time data flow is obtained to form the underwater acoustic images and these images are pre-processed and positions of objects are extracted for SLAM.Extended Kalman filter(EKF)is selected as the kernel approach to enable the underwater vehicle to construct a feature map,and the EKF can locate the underwater vehicle through the map.In order to improve the association effciency,a novel association method based on ant colony algorithm is introduced.Results obtained on simulation data and real acoustic vision data in tank are displayed and discussed.The proposed method maintains better association effciency and reduces navigation error,and is effective and feasible.
文摘起源于移动机器人定位的同步构图定位(simultaneous localization and mapping,SLAM)算法可以克服基于先验图的水下导航算法的不足,实现水下载体的自主导航。本文给出了基于SLAM算法的水下导航算法的系统构建和执行过程,建立了状态方程和观测方程,讨论了新特征加入的问题,并利用模拟数据进行了仿真。仿真结果表明,无论是采用航渡还是区域探索航行方式,该导航算法的定位精度均高于单纯推位方法,具有广阔的应用前景。
