SIMULTANEOUS COOPERATIVE LOCALIZATION FOR AUVs USING RANGE-ONLY SENSORS

Abstract

The absence of GPS underwater makes navigation for autonomous underwater vehicles (AUVs) a challenge. Moreover, the use of static beacons in the form of a long baseline (LBL) array limits the operation area to a few square kilometers and requires substantial deployment effort before operations. In this paper, an algorithm for cooperative localization of AUVs is proposed. We describe a form of cooperative Simultaneous Localization and Mapping (SLAM). Each of the robots in the group is equipped with an Inertial Measurement Unit (IMU) and some of them have a range-only sonar sensor that can determine the relative distance to the others. Two estimators, in the form of a Kalman filter, process the available position information from all the members of the team and produce a pose estimate for every one of them. Simulation results are presented for a typical localization example of three AUVs formation in a large environment and indirect trajectory. The results show that our proposed method offers good localization accuracy, although a small number of low-cost sensors are needed for each vehicle, which validates that it is an economical and practical localization approach.

Fusion-Based Sensor Selection for Optimal State Estimation and Minimum Cost (Intelligent Optimization Approach)

“…As we said before, sensor 1 has a known accuracy and is available, hence R1, 1 P t and S1 Z are non-parametric and take true values. But R2, 2 P t and S2 Z are parametric values. Further details in the calculation of 2 P t are given in Appendix.…”

“…Further details in the calculation of 2 P t are given in Appendix. Also according to (2), f P will be a 2×2 parametric matrix based on…”

“…The Collected observations from various similar or dissimilar sensors can be used in a sensor data fusion system to make a better decision. Many applications and different methods of sensor data fusion are presented in [1][2][3][4][5].…”

Cooperative Uas Localization Using Lowcost Sensors

“…Roumeliotis and Bekey (2002) uses relative position and heading of the robots as the measurements for cooperative localization while Bailey et al(2011) uses laser scanner and bearing measurements for the updation stage. Some of the researchers like Rashidi and Mohammadloo (2011) have used range sensor measurements along with inertial sensors with in the centralized EKF estimation algorithm. Majority of these researchers (Roumeliotis and Bekey 2002, Bailey et al 2011, Rashidi and Mohammadloo 2011 have solved the problem for two dimensional spaces and while neglecting or not using the observations of inertial measurement unit (IMU).…”

“…Some of the researchers like Rashidi and Mohammadloo (2011) have used range sensor measurements along with inertial sensors with in the centralized EKF estimation algorithm. Majority of these researchers (Roumeliotis and Bekey 2002, Bailey et al 2011, Rashidi and Mohammadloo 2011 have solved the problem for two dimensional spaces and while neglecting or not using the observations of inertial measurement unit (IMU). A number of estimation algorithms including UKF (Shi et al 2010), Covariance Intersection (Carrillo-Arce et al 2013), Split Covariance Intersection (Wanasinghe et al 2014, Li andNashashibi 2013), Belief propagation (Savic and Zazo 2013), MAP (Nerukar et al 2009), Iterated Kalman filter (Pillonetto and Carpin 2007) etc.…”