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.
Papers published on a yearly basis
Papers
More filters
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
07 Aug 2019
TL;DR: In this paper, a factor graph optimization method for state estimation is presented, which tightly fuses and smooths inertial navigation, leg odometry and visual odometry to reduce position drift during dynamic motions such as trotting.
Abstract: Legged robots, specifically quadrupeds, are becoming increasingly attractive for industrial applications such as inspection. However, to leave the laboratory and to become useful to an end user requires reliability in harsh conditions. From the perspective of state estimation, it is essential to be able to accurately estimate the robot's state despite challenges such as uneven or slippery terrain, textureless and reflective scenes, as well as dynamic camera occlusions. We are motivated to reduce the dependency on foot contact classifications, which fail when slipping, and to reduce position drift during dynamic motions such as trotting. To this end, we present a factor graph optimization method for state estimation which tightly fuses and smooths inertial navigation, leg odometry and visual odometry. The effectiveness of the approach is demonstrated using the ANYmal quadruped robot navigating in a realistic outdoor industrial environment. This experiment included trotting, walking, crossing obstacles and ascending a staircase. The proposed approach decreased the relative position error by up to 55% and absolute position error by 76% compared to kinematic-inertial odometry.
46 citations
TL;DR: Some new techniques for initial alignment of strapdown inertial navigation system are proposed and it is shown that the initial estimated variance setting of azimuth angle error can influence the speed of initial alignment significantly.
Abstract: Some new techniques for initial alignment of strapdown inertial navigation system are proposed in this paper. A new solution for the precise azimuth alignment is given in detail. A new prefilter, which consists of an IIR filter and a Kalman filter using hidden Markov model, is designed to attenuate the influence of sensor noise and outer disturbance. Navigation algorithm in alignment is modified to feedback continuously for the closed-loop system. It is shown that the initial estimated variance setting of azimuth angle error can influence the speed of initial alignment significantly. At the beginning of alignment, Kalman filter must make a very conservative guess at the initial value of azimuth angle error to get a high convergent speed of the azimuth angle. It is pointed out that the low signal to noise ratio makes the ordinary setting of the estimated azimuth variance slow down the convergent speed of the azimuth angle. Also is shown that the large azimuth angle error problem can be solved well by our solution. The feasibility of these new techniques is verified by simulation and experiment.
46 citations