This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals(TDOA)measurements.The solution uses a piecewise loss function named as mixed Huber loss(MHL)proposed ba...This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals(TDOA)measurements.The solution uses a piecewise loss function named as mixed Huber loss(MHL)proposed based on the classical Huber loss(HL)and its refined version.The MHL is able to effectively mitigate the impact of all levels of measurement outliers by setting two triggering thresholds.In practice,appropriate triggering threshold values can be obtained through simulation given the level of measurement noise and a rough range of potential measurement outliers.A clustering based approach is proposed to further improve the robustness of localization solution against reference sensor related outliers.Simulations are included to examine the solution's performance and compare it with several benchmarks.The proposed MHL based solution is shown to be superior to the classical solution and the benchmarks.The solution is shown to be even robust to multiple measurement outliers.Furthermore,the influence of range measurement outliers in the reference sensor can be effectively mitigated by the clustering based approach.展开更多
The magnetic information measured on the smartphone platform has a large fluctuation and the research of indoor localization algorithm based on smart-phone platform is less. Indoor localization algorithm on smartphone...The magnetic information measured on the smartphone platform has a large fluctuation and the research of indoor localization algorithm based on smart-phone platform is less. Indoor localization algorithm on smartphone platform based on particle filter is studied. Robust local weighted regression is used to smooth the original magnetic data in the process of constructing magnetic map. Use moving average filtering model to filter the online magnetic observation data in positioning process. Compare processed online magnetic data with processed magnetic map collected by smartphone platform and the average matching error is 0.3941uT. Average positioning error is 0.229 meter when using processed online and map data.展开更多
Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as cur...Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature.It also aims to use an extended Kalman filter(EKF)to estimate the fused position of the UAVs and to apply the 2-D splinegon technique to build the map of the complex shaped obstacles.The path of the UAVs are dictated by the Dubins path planning algorithm.The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.Design/methodology/approach–An extended Kalman filter is used to estimate the position of the UAVs,and the 2-D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.Findings-The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2-D splinegon technique.This is a newly proposed algorithm,the most efficient and a robust way in terrain based mapping of the complex obstacles.The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.Originality/value-The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2-D splinegon technique.This is a new approach where the fused EKF estimated positions are used with the limited number of sensors’measurements in building the map of the complex obstacles.展开更多
In point cloud registration applications,noise and poor initial conditions lead to many false matches.False matches significantly degrade registration accuracy and speed.A penalty function is adopted in many robust po...In point cloud registration applications,noise and poor initial conditions lead to many false matches.False matches significantly degrade registration accuracy and speed.A penalty function is adopted in many robust point-to-point registration methods to suppress the influence of false matches.However,after applying a penalty function,problems cannot be solved in their analytical forms based on the introduction of nonlinearity.Therefore,most existing methods adopt the descending method.In this paper,a novel iterative-reweighting-based method is proposed to overcome the limitations of existing methods.The proposed method iteratively solves the eigenvectors of a four-dimensional matrix,whereas the calculation of the descending method relies on solving an eight-dimensional matrix.Therefore,the proposed method can achieve increased computational efficiency.The proposed method was validated on simulated noise corruption data,and the results reveal that it obtains higher efficiency and precision than existing methods,particularly under very noisy conditions.Experimental results for the KITTI dataset demonstrate that the proposed method can be used in real-time localization processes with high accuracy and good efficiency.展开更多
This paper describes a brain-inspired simultaneous localization and mapping （SLAM） system using oriented features from accelerated segment test and rotated binary robust independent elementary （ORB） features of R...This paper describes a brain-inspired simultaneous localization and mapping （SLAM） system using oriented features from accelerated segment test and rotated binary robust independent elementary （ORB） features of RGB （red, green, blue） sensor for a mobile robot. The core SLAM system, dubbed RatSLAM, can construct a cognitive map using information of raw odometry and visual scenes in the path traveled. Different from existing RatSLAM system which only uses a simple vector to represent features of visual image, in this paper, we employ an efficient and very fast descriptor method, called ORB, to extract features from RCB images. Experiments show that these features are suitable to recognize the sequences of familiar visual scenes. Thus, while loop closure errors are detected, the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm. Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.展开更多
基金supported by the National Natural Science Foundation of China(U20B2038)。
文摘This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals(TDOA)measurements.The solution uses a piecewise loss function named as mixed Huber loss(MHL)proposed based on the classical Huber loss(HL)and its refined version.The MHL is able to effectively mitigate the impact of all levels of measurement outliers by setting two triggering thresholds.In practice,appropriate triggering threshold values can be obtained through simulation given the level of measurement noise and a rough range of potential measurement outliers.A clustering based approach is proposed to further improve the robustness of localization solution against reference sensor related outliers.Simulations are included to examine the solution's performance and compare it with several benchmarks.The proposed MHL based solution is shown to be superior to the classical solution and the benchmarks.The solution is shown to be even robust to multiple measurement outliers.Furthermore,the influence of range measurement outliers in the reference sensor can be effectively mitigated by the clustering based approach.
文摘The magnetic information measured on the smartphone platform has a large fluctuation and the research of indoor localization algorithm based on smart-phone platform is less. Indoor localization algorithm on smartphone platform based on particle filter is studied. Robust local weighted regression is used to smooth the original magnetic data in the process of constructing magnetic map. Use moving average filtering model to filter the online magnetic observation data in positioning process. Compare processed online magnetic data with processed magnetic map collected by smartphone platform and the average matching error is 0.3941uT. Average positioning error is 0.229 meter when using processed online and map data.
文摘Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature.It also aims to use an extended Kalman filter(EKF)to estimate the fused position of the UAVs and to apply the 2-D splinegon technique to build the map of the complex shaped obstacles.The path of the UAVs are dictated by the Dubins path planning algorithm.The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.Design/methodology/approach–An extended Kalman filter is used to estimate the position of the UAVs,and the 2-D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.Findings-The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2-D splinegon technique.This is a newly proposed algorithm,the most efficient and a robust way in terrain based mapping of the complex obstacles.The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.Originality/value-The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2-D splinegon technique.This is a new approach where the fused EKF estimated positions are used with the limited number of sensors’measurements in building the map of the complex obstacles.
基金the National Natural Science Foundation of China(No.U1764264)。
文摘In point cloud registration applications,noise and poor initial conditions lead to many false matches.False matches significantly degrade registration accuracy and speed.A penalty function is adopted in many robust point-to-point registration methods to suppress the influence of false matches.However,after applying a penalty function,problems cannot be solved in their analytical forms based on the introduction of nonlinearity.Therefore,most existing methods adopt the descending method.In this paper,a novel iterative-reweighting-based method is proposed to overcome the limitations of existing methods.The proposed method iteratively solves the eigenvectors of a four-dimensional matrix,whereas the calculation of the descending method relies on solving an eight-dimensional matrix.Therefore,the proposed method can achieve increased computational efficiency.The proposed method was validated on simulated noise corruption data,and the results reveal that it obtains higher efficiency and precision than existing methods,particularly under very noisy conditions.Experimental results for the KITTI dataset demonstrate that the proposed method can be used in real-time localization processes with high accuracy and good efficiency.
基金supported by National Natural Science Foundation of China(No.61673283)
文摘This paper describes a brain-inspired simultaneous localization and mapping （SLAM） system using oriented features from accelerated segment test and rotated binary robust independent elementary （ORB） features of RGB （red, green, blue） sensor for a mobile robot. The core SLAM system, dubbed RatSLAM, can construct a cognitive map using information of raw odometry and visual scenes in the path traveled. Different from existing RatSLAM system which only uses a simple vector to represent features of visual image, in this paper, we employ an efficient and very fast descriptor method, called ORB, to extract features from RCB images. Experiments show that these features are suitable to recognize the sequences of familiar visual scenes. Thus, while loop closure errors are detected, the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm. Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.
