Despite recent advances in semantic Simultaneous Localization and Mapping (SLAM) for terrestrial and aerial applications, underwater semantic SLAM remains an open and largely unaddressed research problem due to the unique sensing modalities and the object classes found underwater. This paper presents an object-based semantic SLAM method for underwater environments that can identify, localize, classify, and map a wide variety of marine objects without a priori knowledge of the object classes present in the scene. The method performs unsupervised object segmentation and object-level feature aggregation, and then uses opti-acoustic sensor fusion for object localization. Probabilistic data association is used to determine observation to landmark correspondences. Given such correspondences, the method then jointly optimizes landmark and vehicle position estimates. Indoor and outdoor underwater datasets with a wide variety of objects and challenging acoustic and lighting conditions are collected for evaluation and made publicly available. Quantitative and qualitative results show the proposed method achieves reduced trajectory error compared to baseline methods, and is able to obtain comparable map accuracy to a baseline closed-set method that requires hand-labeled data of all objects in the scene.

We present the Perseus autonomous underwater vehicle (AUV) - an A-sizedaaA-size stands for the standard sonobuoy [2] form factor, with a maximum diameter of 124 mm and a length of around 0.9 m, ensuring the ability to launch from standard sonobuoy launchers onboard a wide array of fixed wing and rotary wing air crafts, surface ships and submarines [3] micro AUV, outfitted with a low-cost passive inverted ultra-short baseline (piUSBL) acoustic reception system, which allows it to acoustically track-and-trail a leader vehicle that carries an acoustic transmission source. With a long-term goal of linear convoying of multiple A-sized AUVs, in this work, we used an unmanned surface vehicle (USV) towing an acoustic source, as a proxy for a lead AUV, demonstrating that the Perseus AUV is able to successfully track-and-trail a leader vehicle. The AUV was also outfitted with a tuna-inspired morphing fin mechanism that allowed the vehicle to achieve good directional stability as well as good maneuverability; properties that are useful for linear convoying AUVs, but are presently difficult to achieve because they impose contradictory requirements. We demonstrated this system with real-world, in-water experiments in the Charles river, Massachusetts, USA.

This paper presents a method for processing sparse, non-Gaussian multimodal data in a simultaneous localization and mapping (SLAM) framework using factor graphs. Our approach demonstrates the feasibility of using a sum-product inference strategy to recover functional belief marginals from highly non-Gaussian situations, relaxing the prolific unimodal Gaussian assumption. The method is more focused than conventional multi-hypothesis approaches, but still captures dominant modes via multi-modality. The proposed algorithm exists in a trade space that spans the anticipated uncertainty of measurement data, task-specific performance, sensor quality, and computational cost. This work leverages several major algorithm design constructs, including clique recycling, to put an upper bound on the allowable computational expense - a major challenge in non-parametric methods. To better demonstrate robustness, experimental results show the feasibility of the method on at least two of four major sources of non-Gaussian behavior: i) the first introduces a canonical range-only problem which is always underdetermined although composed exclusively from Gaussian measurements; ii) a real-world AUV dataset, demonstrating how ambiguous acoustic correlator measurements are directly incorporated into a non-Gaussian SLAM solution, while using dead reckon tethering to overcome short term computational requirements.

Synthetic Aperture Sonar (SAS) is a technique to improve the spatial resolution from a moving set of receivers by extending the array in time, increasing the effective array length and aperture. This technique is limited by the accuracy of the receiver position estimates, necessitating highly accurate, typically expensive aided-inertial navigation systems for submerged platforms. We leverage simultaneous localization and mapping to fuse acoustic and navigational measurements and obtain accurate pose estimates even without the benefit of absolute positioning for lengthy underwater missions. We demonstrate a method of formulating the well-known SAS problem in a SLAM framework, using acoustic data from hydrophones to simultaneously estimate platform and beacon position. An empirical probability distribution is computed from a conventional beamformer to correctly account for uncertainty in the acoustic measurements. The non-parametric method relieves the familiar Gaussian-only assumption currently used in the localization and mapping discipline and fits effectively into a factor graph formulation with conventional factors such as ground-truth priors and odometry. We present results from field experiments performed on the Charles River with an autonomous surface vehicle which demonstrate simultaneous localization of an unknown acoustic beacon and vehicle positioning, and provide comparison to GPS ground truths.

The underwater environment poses significant challenges for accurate autonomous underwater vehicle (AUV) navigation. Electromagnetic (EM) waves rapidly attenuate due to absorption by water, thereby preventing the use of traditional EM -based positioning methods such as Global Positioning System (GPS) or visible -light cameras. Consequently, underwater positioning is often performed using systems that operate in the hydro -acoustic frequency range (<; 10 MHz). Recent work has demonstrated the efficacy of a novel acoustic positioning approach for multi-AUV operations called passive inverted ultra -short baseline (piUSBL) localization - with each vehicle equipped with a time -synchronized USBL array, oneway travel -time (OWTT) range and angle between the AUV and a single acoustic beacon enables multi-AUV navigation relative to the beacon. In this work, a piUSBL system using a fi ve-hydrophone pyramidal array implemented on a WAM-V autonomous surface vehicle (ASV) was used to experimentally gather acoustic measurements and to compare the accuracy of piUSBL localization against ground -truth from a differential GPS unit. This paper provides a comprehensive analysis of the positioning accuracy of the system in a real -world environment, both prior to and after Bayesian fi ltering, using two independent acoustic beacons for validation. We demonstrate that piUSBL provides acoustic range and angle measurements with errors of about p, ± cr = 0.03 ± 1.49 m and p, ± cr = -0.11 ± 3.16 ° respectively. These experimental results suggest that piUSBL localization can provide a highly accurate, inexpensive, and low power navigation solution for the next generation of miniature, low-cost underwater vehicle.

Accurate localization is critical for a robotic vehicle to navigate autonomously. Conventional autonomous underwater vehicles (AUV s) typically rely on an inertial navigation system (INS) aided by a Doppler velocity log (DVL) in order to reduce the rate of positional error growth of dead-reckoning to a level suitable for reliable navigation underwater. The size, cost, and power requirements of these systems result in vehicles that are prohibitively large and expensive for multi-AUV operations. In this work we present the first results of closed-loop experiments using a miniature, low-cost SandShark AUV and a custom-designed, inexpensive acoustic system first described in our previous work. Results are validated using an independent LBL system, and indicate that our approach is suitably accurate to enable the self-localization of such AUVs without the use of an expensive DVL-aided INS. Self-localization is performed by obtaining acoustic range and angle measurements from the AUV to a single acoustic beacon using a vehicle-mounted passive hydrophone receiver-array, and fusing these measurements using a particle filter. A critical aspect of our approach that allows for real-time, closed-loop operation is the close coupling of conventional phased-array beamforming and particle filtering - this implementation detail reduces the computational complexity associated with our previously described two-stage beamforming plus particle filtering process, and consequently also enables an increase in particle count and an improvement in navigational accuracy. Experimental results are provided for two cases: first, absolute navigation in the case where the beacon is fixed at a known position; and second, relative navigation with a moving beacon, a novel operating paradigm for AUVs which promises to enable multi-AUV operations while maintaining bounded navigation error.

This paper presents an acoustic localization system for small and low-cost autonomous underwater vehicles (AUVs). Accurate and robust localization for low-cost AUVs would lower the barrier toward multi-AUV research in river and ocean environments. However, these AUVs introduce size, power, and cost constraints that prevent the use of conventional AUV sensors and acoustic positioning systems, adding great difficulty to the problem of underwater localization. Our system uses a single acoustic transmitter placed at a reference point and is acoustically passive on the AUV, reducing cost and power use, and enabling multi-AUV localization. The AUV has an ultra-short baseline (USBL) receiver array that uses one-way travel-time (OWTT) and phased-array beamforming to calculate range, azimuth, and inclination to the transmitter, providing an instantaneous estimate of the vehicle location. This estimate is fed to a particle filter and graph-based smoothing algorithm to generate a consistent AUV trajectory. We describe the complete processing pipeline of our system, and present results based on experiments using a low-cost AUV. To the authors' knowledge, this work constitutes the first practical demonstration of the feasibility of OWTT inverted USBL navigation for AUVs.