Sensor-fusion based navigation attracts significant attentions for its robustness and accuracy in various applications. To achieve a versatile and efficient state estimation both indoor and outdoor, this paper present...Sensor-fusion based navigation attracts significant attentions for its robustness and accuracy in various applications. To achieve a versatile and efficient state estimation both indoor and outdoor, this paper presents an improved monocular visual inertial navigation architecture within the Multi-State Constraint Kalman Filter （MSCKF）. In addition, to alleviate the initialization demands by appending enough stable poses in MSCKF, a rapid and robust Initialization MSCKF （I-MSCKF） navigation method is proposed in the paper. Based on the trifocal tensor and sigmapoint filter, the initialization of the integrated navigation can be accomplished within three consecutive visual frames. Thus, the proposed I-MSCKF method can improve the navigation performance when suffered from shocks at the initial stage. Moreover, the sigma-point filter is applied at initial stage to improve the accuracy for state estimation. The state vector generated at initial stage from the proposed method is consistent with MSCKF, and thus a seamless transition can be achieved between the initialization and the subsequent navigation in I-MSCKF. Finally, the experimental results show that the proposed I-MSCKF method can improve the robustness and accuracy for monocular visual inertial navigations.展开更多
This paper investigates the problem of real-time estimation for one kind of linear time invariant systems which subject to limited communication capacity. The communication limitations include signal transmission dela...This paper investigates the problem of real-time estimation for one kind of linear time invariant systems which subject to limited communication capacity. The communication limitations include signal transmission delay, the out-of-sequence measurements and data packet dropout, which appear typically in a network environment. The kernel of filter design is equally to formularize the traditional Kalman filter as one linear weighted summation which is composed of the initial state estimate and all sequential sampled measurements. For it can adapt aforementioned information limitations, the linear weighted summation is then decomposed into two stages. One is a predict-estimator composed by all reached measurements, another is one compensator constructed by those time-delayed data. In the network environment, there are obvious differences between the new hybrid filter and those existing delayed Kalman filters. For example, the novel filter can be optimal in the sense of linear minimum mean square error as soon as all measurements available and has the lowest running time than these existing delayed filters. One simulation, including two cases, is utilized to illustrate the design procedures proposed in this paper.展开更多
Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target pursuit.In this paper,we focus on a p...Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target pursuit.In this paper,we focus on a problem that is very specific yet we believe important.That is,from the vision measurements,we can formulate various measurements.Which and how the measurements should be used?These problems are very fundamental,but we notice that practitioners usually do not pay special attention to them and often make mistakes.Motivated by this,we formulate three pseudo-linear measurements based on the bearing and angle measurements,which are standard vision measurements that can be obtained.Different estimators based on Kalman filtering and least-squares estimation are established and compared based on numerical experiments.It is revealed that correctly analyzing the covariance noises is critical for the Kalman filtering-based estimators.When the variance of the original measurement noise is unknown,the pseudo-linear least-squares estimator that has the smallest magnitude of the transformed noise can be a good choice.展开更多
In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel...In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel information filtering structure.It is shown that the proposed distributed fusion estimator has smaller estimation error covariance and less computation complexity when compared with the centralised Kalman like estimator with multiple intermittent measurements.展开更多
基金the supports of the Beijing Key Laboratory of Digital Design&Manufacturethe Academic Excellence Foundation of Beihang University for Ph.D.Studentsthe MIIT(Ministry of Industry and Information Technology)Key Laboratory of Smart Manufacturing for High-end Aerospace Products
文摘Sensor-fusion based navigation attracts significant attentions for its robustness and accuracy in various applications. To achieve a versatile and efficient state estimation both indoor and outdoor, this paper presents an improved monocular visual inertial navigation architecture within the Multi-State Constraint Kalman Filter （MSCKF）. In addition, to alleviate the initialization demands by appending enough stable poses in MSCKF, a rapid and robust Initialization MSCKF （I-MSCKF） navigation method is proposed in the paper. Based on the trifocal tensor and sigmapoint filter, the initialization of the integrated navigation can be accomplished within three consecutive visual frames. Thus, the proposed I-MSCKF method can improve the navigation performance when suffered from shocks at the initial stage. Moreover, the sigma-point filter is applied at initial stage to improve the accuracy for state estimation. The state vector generated at initial stage from the proposed method is consistent with MSCKF, and thus a seamless transition can be achieved between the initialization and the subsequent navigation in I-MSCKF. Finally, the experimental results show that the proposed I-MSCKF method can improve the robustness and accuracy for monocular visual inertial navigations.
基金Supported by the National Natural Science Foundation of China (No.60804064,60772006)
文摘This paper investigates the problem of real-time estimation for one kind of linear time invariant systems which subject to limited communication capacity. The communication limitations include signal transmission delay, the out-of-sequence measurements and data packet dropout, which appear typically in a network environment. The kernel of filter design is equally to formularize the traditional Kalman filter as one linear weighted summation which is composed of the initial state estimate and all sequential sampled measurements. For it can adapt aforementioned information limitations, the linear weighted summation is then decomposed into two stages. One is a predict-estimator composed by all reached measurements, another is one compensator constructed by those time-delayed data. In the network environment, there are obvious differences between the new hybrid filter and those existing delayed Kalman filters. For example, the novel filter can be optimal in the sense of linear minimum mean square error as soon as all measurements available and has the lowest running time than these existing delayed filters. One simulation, including two cases, is utilized to illustrate the design procedures proposed in this paper.
文摘Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target pursuit.In this paper,we focus on a problem that is very specific yet we believe important.That is,from the vision measurements,we can formulate various measurements.Which and how the measurements should be used?These problems are very fundamental,but we notice that practitioners usually do not pay special attention to them and often make mistakes.Motivated by this,we formulate three pseudo-linear measurements based on the bearing and angle measurements,which are standard vision measurements that can be obtained.Different estimators based on Kalman filtering and least-squares estimation are established and compared based on numerical experiments.It is revealed that correctly analyzing the covariance noises is critical for the Kalman filtering-based estimators.When the variance of the original measurement noise is unknown,the pseudo-linear least-squares estimator that has the smallest magnitude of the transformed noise can be a good choice.
基金supported by the National Natural Science Foundation of China(61473306).
文摘In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel information filtering structure.It is shown that the proposed distributed fusion estimator has smaller estimation error covariance and less computation complexity when compared with the centralised Kalman like estimator with multiple intermittent measurements.
