 While critical to the simultaneous localization and mapping (SLAM) process data association can be unreliable and especially so in the noisy, dynamic underwater environment. This presentation discusses a data association algorithm to enhance underwater SLAM on autonomous underwater vehicles (AUV) with side-scan sonars through jointly associating the estimated relative position of a landmark to the AUV with the local seafloor elevation gradients surrounding the landmark. The local elevation gradients are extracted from the same side-scan sonar images that the landmarks are detected in. As environmental features, the gradients are less susceptible to gross changes due to the underwater environment compared to a small (~ few meters) landmark. This scheme yields realistic correct associations when implemented in MOOS and validated in a hardware-in-the-loop AUV simulator using sidescan sonar data from earlier trials. The data association algorithm is now integrated into the iSAM (incremental smoothing and mapping) dynamic pose graph formulation and undergoing in-water validation as an on-line MOOS capability. Restore June 00, 0000, at 00:00 AM by 000.0.0.0 - Changed lines 0-0 from: !!!! %color=#0000BB% [[Talk.00-ValSchmidt | Prev-Talk]]%% | \ %color=#0000BB%[[Talk.00-Seto | Next-Talk]]%% | \ to: !!!! %color=#0000BB% [[Talk.00-Keane | Prev-Talk]]%% | \ %color=#0000BB%[[Talk.00-Seto | Next-Talk]]%% | \ Changed lines 00-00 from: !! Talk-00: ''A Flexible, Low-Cost MOOS-IvP Based Platform for Marine Autonomy Research'' !!!!%color=#000000% '''James Keane, Australian Maritime College, University of Tasmania, Royal Australian Navy''' !!!!%color=#000000% '''Alexander Forrest, Australian Maritime College, University of Tasmania, Tahoe Environmental Research Center, University of California.''' !!!!%color=#000000% ''' Hordur Johannsson, Teledyne Gavia''' !!!!%color=#000000% '''Jonathan Duffy, Australian Maritime College, University of Tasmania''' !!!!%color=#000000% '''David Battle, Australian Defence Science Technology Organisation.''' Homing behavior for Autonomous Underwater Vehicles (AUVs) is vital for autonomous multi-platform docking and also indispensable for recovery of vehicles deployed beneath ice shelves. Mission Oriented Operating Suite (MOOS) is currently being implemented as a backseat driver on a Teledyne Gavia AUV to enhance the AUV with adaptive manoeuvring capabilities; in turn enabling homing to a single LinkQuest 0000 Long BaseLine (LBL) acoustic transponder. A homing application (pHomeToLBL) has been developed in the MOOS simulator which uses LBL Range Reports to estimate transponder location, and hence update vehicle return waypoints through the MOOS database (MOOSDB). The trilateration-based homing algorithm was applied to raw mission data (from Antarctic sub-ice deployments) where two transponder were present and consistently located deployed LBLs with an accuracy linearly proportional to the uncertainty of the LBL Range Reports. Simultaneously, collaboration with Teledyne Gavia software engineers is leading to the compilation of MOOS-IvP (with the pHomeToLBL application) on the Gavia main vehicle computer. Field trials to demonstrate MOOS-IvP-GAVIA and homing are scheduled for June 0000. Field trials will be an industry-first of deploying a user-developed application on MOOS-IvP-GAVIA, and furthermore, of having a Gavia enhanced with adaptive manoeuvring capabilities for homing. Ultimately, the MOOS-IvP-GAVIA backseat driver will enable this AUV to home to an acoustic transponder (stationary or underway) but the trilateration algorithm could be used on any AUV with MOOS as a backseat driver in preparation for autonomous docking. 