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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Patent•
19 Mar 1996
TL;DR: In this paper, a transfer alignment filter is applied to a sensor inertial navigation system to correct the pod LOS relative to the navigation reference frame, which may operate directly upon the pseudo ranges and delta pseudo ranges to satellites being tracked by the GPS receiver.
Abstract: A FLIR boresight alignment system (52) for aligning a sensor pod LOS associated with a weapons pod of a fighter aircraft to a navigation reference frame. A pod inertial navigation and global positioning system (62) provides position, velocity and attitude of a sensor (58) within the pod. An aircraft inertial navigation and/or global positioning system (68) provides position, velocity and attitude of the aircraft. The sensor position and velocity and the aircraft position and velocity are applied to a transfer alignment filter (64) that utilizes Kalman filtering. An output of the transfer alignment filter (64) is applied to a sensor inertial navigation system to correct the pod LOS relative to the navigation reference frame. Alternately, the transfer alignment filter (64) may operate directly upon the pseudo ranges and delta pseudo ranges to satellites being tracked by the GPS receiver.
56 citations
TL;DR: The localization problem with online error correction (OEC) modules that are trained to correct a vision-aided localization network's mistakes are addressed and the generalizability of the OEC modules are demonstrated.
Abstract: While numerous deep approaches to the problem of vision-aided localization have been recently proposed, systems operating in the real world will undoubtedly experience novel sensory states previously unseen even under the most prodigious training regimens. We address the localization problem with online error correction (OEC) modules that are trained to correct a vision-aided localization network's mistakes. We demonstrate the generalizability of the OEC modules and describe our unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU measurements and generate hypothesis trajectories which are then corrected online according to the Jacobians of scaled image projection errors with respect to spatial grids of pixel coordinates. We evaluate our network against state-of-the-art (SoA) VIO, visual odometry (VO), and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI Odometry dataset as well as a micro aerial vehicle (MAV) dataset that we collected in the AirSim simulation environment. We demonstrate better than SoA translational localization performance against comparable SoA approaches on our evaluation sequences.
56 citations
TL;DR: The proposed system gives key gait analysis parameters such as step length, stride length, foot angle and walking speed and also it gives three dimensional trajectories of two feet for gaitAnalysis.
Abstract: Two feet motion is estimated for gait analysis. An inertial sensor is attached on each shoe and an inertial navigation algorithm is used to estimate the movement of both feet. To correct inter-shoe position error, a camera is installed on the right shoe and infrared LEDs are installed on the left shoe. The proposed system gives key gait analysis parameters such as step length, stride length, foot angle and walking speed. Also it gives three dimensional trajectories of two feet for gait analysis.
56 citations
TL;DR: A new particle filter named central difference particle filter (CDPF) is introduced and applied to the transfer alignment, and the superiorities of the proposed approach over EKPF and UKPF are shown.
Abstract: For the strapdown inertial navigation system (SINS) on vertically launched and warship-borne missiles, the transfer alignment is an effective approach to estimate its navigation attitudes at the time of launching missiles, which is also called the initial navigation attitudes of SINS. The quaternions are adopted to describe attitudes, and a transfer alignment model with this description is established. However, due to the strong nonlinearity of the alignment model, the non-Gaussian distributions of gyros drifts, and the demands for alignment speed and precision, it poses a great challenge to the estimation of the initial navigation attitudes of SINS. In order to solve this problem, a new particle filter (PF) named central difference particle filter (CDPF) is introduced and applied to the transfer alignment. In this new filter, the central difference filter is used to generate proposal distribution for sequential importance sampling. A comparison study regarding the performance of CDPF with those of the extended Kalman particle filter (EKPF) and the unscented Kalman particle filter (UKPF) is conducted. The simulation results show the superiorities of the proposed approach over EKPF and UKPF.
56 citations
TL;DR: This work addresses the accurate estimation of the IMU mounting angles through an aided dead reckoning (DR) approach and demonstrates that the pitch and heading mounting angles can be estimated with a comparable accuracy with the GNSS/INS attitude solution.
Abstract: Nonholonomic constraint (NHC) and odometer speed have been proven to significantly improve the navigation accuracy of a global navigation satellite system (GNSS)-aided inertial navigation system (INS) for land vehicular applications. Exploiting the full potential of the NHC and odometer aids requires the inertial measurement unit (IMU) mounting angles, i.e., angular misalignment with respect to the host vehicle, to be precisely known. We address the accurate estimation of the IMU mounting angles through an aided dead reckoning (DR) approach. In this method, DR using the GNSS/INS integrated attitude and distance traveled is fused with the GNSS/INS integrated position through a straightforward Kalman filter. Simulation and field tests are carried out to validate the proposed algorithm for different grade IMUs, including typical navigation-grade, tactical-grade and low-cost IMUs. The results demonstrate that the pitch and heading mounting angles can be estimated with a comparable accuracy with the GNSS/INS attitude solution, for example, 0.001° accuracy can be achieved for a navigation-grade GNSS/INS integrated system. The roll mounting angle can not be estimated due to lack of observability in this approach, and the heading mounting angle estimation may be influenced by the GNSS/INS heading accuracy drift to some extent for the low-cost IMUs.
56 citations