A lightweight odometry network for GNSS/INS integration during GNSS outages

被引:3
|
作者
Yu, Ziyan [1 ]
Jiang, Jinguang [1 ,2 ]
Yan, Peihui [2 ]
Li, Yuying [2 ]
Wu, Jiaji [2 ]
Xie, Dongpeng [3 ]
机构
[1] Wuhan Univ, Inst Artificial Intelligence, Sch Comp Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China
[2] Wuhan Univ, GNSS Res Ctr, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China
[3] Wuhan Univ, Elect Informat Sch, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China
关键词
GNSS/INS integrated navigation system; Deep Neural Network; Wheeled odometer; Inertial odometry; COVARIANCE ESTIMATION; NEURAL-NETWORK; KALMAN FILTER; NAVIGATION; ALGORITHM; GPS/INS; HYBRID; FUSION;
D O I
10.1016/j.asoc.2023.111143
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In challenging environments like urban canyons and tunnels, the Global Navigation Satellite System (GNSS) signal can be interrupted. When this happens, the integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation system relies solely on INS, resulting in rapid dispersion of positioning accuracy over time. Incorporating odometer information into a filter algorithm is one potential solution for correcting INS errors and improving navigation accuracy. However, this approach increases the cost and power consumption of the system. To implement an odometer-assisted integrated navigation system without inflating cost and power consumption, we propose a lightweight odometer network, LONet. This network can emulate a wheeled odometer, determine the carrier's forward velocity using Inertial Measurement Unit (IMU) output data, and utilize non-holonomic constraint (NHC) and zero velocity update (ZUPT) to maintain the system's positioning accuracy. To evaluate the performance of our network, we conducted velocity estimate experiments across different velocity intervals. The results demonstrated that our network requires fewer parameters and produces lower errors in estimated velocity compared to state-of-the-art networks. Furthermore, we integrated LONet into the navigation system to mitigate the effects of GNSS signal outages. The results show that the LONet-assisted integrated navigation system achieved horizontal errors comparable to, and sometimes lower than, those obtained using a real wheeled odometer.
引用
收藏
页数:11
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