Closed-loop terrain relative navigation for AUVs with non-inertial grade navigation sensors

Abstract

Terrain Relative Navigation (TRN) has the potential to enable drift-free, low-cost navigation for a wide range of underwater vehicles. Current underwater TRN systems have demonstrated meter-level navigation accuracy by utilizing high accuracy inertial navigation systems in combination with both low and high quality sonar sensors and bathymetry maps. No studies, however, have considered the application of underwater TRN to vehicles with non-inertial-grade navigation systems, an extension which would greatly increase its applicability. The use of low-grade motion sensors in TRN is problematic due to the presence of large errors which can cause highly inaccurate alignment of successive sonar pings. To address this, a TRN filter is developed in which information present in the terrain correlations is used to help identify and mitigate large motion sensor errors. The resulting filter consists of a tight coupling between TRN and the onboard navigation, yielding a significantly improved navigation solution. The development of this TRN system is detailed for an AUV with an Attitude, Heading and Reference System (AHRS)-based dead reckoning navigation, which has an observed accuracy of 5 -25% distance traveled.Field trials in Monterey Bay, CA demonstrate the ability of the TRN filter to acheive 5 -10m navigational precision. Field trials are also presented demonstrating successful real-time, closed-loop TRN on the AUV. For these trials, the combined TRN/AHRS system enabled the vehicle to fly to a known beacon location with ability comparable to an acoustic homing system. The presented results demonstrate that TRN technology can successfully be applied to non-inertial-grade vehicle platforms, greatly improving the navigation capabilities of such systems.

Experimental results of a Terrain Relative Navigation algorithm using a simulated lunar scenario

“…This technique is used when GPS is not available, such as for cruise missile navigation or space robot localization [14]. TRN has been used and tested also with helicopters [15] and Autonomous Underwater Vehicles (AUVs) [16].…”

Construction Method of the Topographical Features Model for Underwater Terrain Navigation

“…One core of terrain matching aided navigation system is the characteristics of the terrain [6,7]. As local statistical characteristics of the terrain, terrain information is a property of terrain itself, which is independent of the specific terrain matching algorithm [8,9].…”

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

“…Bathymetry-based localization generally employs sequential Bayesian filtering to estimate the probability of a vehicle being at a particular location in the map, using process and measurement models (Meduna et al 2010;Carreno et al 2010;Kalyan and Chitre 2013). The measurement model can be updated using two different approaches: batch or recursive.…”

“…In most marine applications, the data for the vehicle's measurement model are provided by on-board multi-beam echo sounders (Nygren and Jansson 2004;Fairfield and Wettergreen 2008;Meduna et al 2010). This enables multiple simultaneous altimeter measurement at every time step and improves the filter's performance.…”

“…Bathymetry-based localization and navigation, also known as terrain relative navigation (TRN) (Meduna et al 2010), terrain based navigation (TBN), terrain-aided navigation (TAN) (Carreno et al 2010), and bathymetric-aided navigation (BAN) (Kalyan and Chitre 2013) has been used on autonomous underwater vehicles (AUVs) for underwater navigation. Given a bathymetric map, the idea of bathymetrybased localization is essentially to match water depth measurements with the map, in order to estimate the vehicle's position.…”