Topic
Inertial navigation system
About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.
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TL;DR: A hybrid visual inertial navigation algorithm for an autonomous and intelligent vehicle that combines the multi-state constraint Kalman filter (MSCKF) with the nonlinear visual-inertial graph optimization with the design of a novel measurement model that exploits all of the measurements and information available within a sliding window.
Abstract: In this paper, we present a hybrid visual inertial navigation algorithm for an autonomous and intelligent vehicle that combines the multi-state constraint Kalman filter (MSCKF) with the nonlinear visual-inertial graph optimization. The MSCKF is a well-known visual inertial odometry (VIO) method that performs the fusion between an inertial measurement unit (IMU) and the image measurements within a sliding window. The MSCKF computes the re-projection errors from the camera measurements and the states in the sliding window. During this process, the structure-only estimation is performed without exploiting the full information over the window, like the relative interstate motion constraints and their uncertainties. The key contribution of this paper is combination of the filtering and non-linear optimization method for VIO, and the design of a novel measurement model that exploits all of the measurements and information available within the sliding window. The local visual-inertial optimization is performed using pre-integrated IMU measurements and camera measurements. It infers the probabilistically optimal relative pose constraints. These local optimal constraints are used to estimate the global states under the MSCKF framework. The proposed local-optimal-multi-state constraint Kalman filter is validated using a simulation data set, as well as publicly available real-world data sets generated from real-world urban driving experiments.
46 citations
TL;DR: This paper adopts an estimation method using time evaluation of the system's state transition matrix and utilizes neural network ensembles to deal with the Kalman filter, which demonstrates validity of the proposed method and clearly shows that integrated navigation solution can be used for extended periods without degradation.
46 citations
TL;DR: Results indicate that the proposed UAV system achieves meter-level accuracy and reconstructs the environment with dense point cloud, as well as taking the feedback bias into INS/GNSS.
Abstract: In disaster management, reconstructing the environment and quickly collecting the geospatial data of the impacted areas in a short time are crucial. In this letter, a light detection and ranging (LiDAR)-based unmanned aerial vehicle (UAV) is proposed to complete the reconstruction task. The UAV integrate an inertial navigation system (INS), a global navigation satellite system (GNSS) receiver, and a low-cost LiDAR. An unmanned helicopter is introduced and the multisensor payload architecture for direct georeferencing is designed to improve the capabilities of the vehicle. In addition, a new strategy of iterative closest point algorithm is proposed to solve the registration problems in the sparse and inhomogeneous derived point cloud. The proposed registration algorithm addresses the local minima problem by the use of direct-georeferenced points and the novel hierarchical structure as well as taking the feedback bias into INS/GNSS. The generated point cloud is compared with a more accurate one derived from a high-grade terrestrial LiDAR which uses real flight data. Results indicate that the proposed UAV system achieves meter-level accuracy and reconstructs the environment with dense point cloud.
46 citations
TL;DR: In this paper, a constructive neural network (cascade-correlation network (CCNs)) is proposed to overcome the limitations of conventional techniques that are predominantly based on the Kalman filter (KF).
Abstract: This article exploits the idea of developing an alternative data fusion scheme that integrates the outputs of low-cost micro-electro-mechanical systems (MEMS) inertial measurements units (IMUs) and receivers of the global positioning system (GPS). The proposed scheme is implemented using a constructive neural network (cascade-correlation network (CCNs)) to overcome the limitations of conventional techniques that are predominantly based on the Kalman filter (KF). The CNN applied in this research has the advantage of having a flexible topology if compared with the recently utilized multi-layer feed-forward neural networks (MFNNs) for inertial navigation system (INS)/GPS integration. The preliminary results presented in this article illustrate the effectiveness of proposed CCNs over both MFNN-based and Kalman filtering techniques for INS/GPS integration.
46 citations
TL;DR: The results of theory and experiment show that the split and reuse type LDV has great advantages of high accuracy and signal-to-noise ratio, which has greatly enhanced the position accuracy of the navigation system.
Abstract: In order to suppress the error accumulation effect of inertial navigation system (INS), an idea of building an integrated navigation system using a laser Doppler velocimeter (LDV) together with strapdown inertial navigation (SIN) is proposed. The basic principle of LDV is expounded while a novel LDV with advanced optical structure is designed based on the split and reuse technique, and the process of dead reckoning using an integrated system which consists of LDV and SIN is discussed detailedly. The results of theory and experiment show that: the split and reuse type LDV has great advantages of high accuracy and signal-to-noise ratio, which has greatly enhanced the position accuracy of the navigation system. The position error has been decreased from 1166 m in 2 h with pure SIN to 20 m in 2 h with the integrated system.
46 citations