Data-driven Loop Closure Detection in Bathymetric Point Clouds for Underwater SLAM

Simultaneous localization and mapping (SLAM) frameworks for autonomous navigation rely on robust data association to identify loop closures for back-end trajectory optimization. In the case of autonomous underwater vehicles (AUVs) equipped with multibeam echosounders (MBES), data association is particularly challenging due to the scarcity of identifiable landmarks in the seabed, the large drift in dead-reckoning navigation estimates to which AUVs are prone and the low resolution characteristic of MBES data. Deep learning solutions to loop closure detection have shown excellent performance on data from more structured environments. However, their transfer to the seabed domain is not immediate and efforts to port them are hindered by the lack of bathymetric datasets. Thus, in this paper we propose a neural network architecture aimed to showcase the potential of adapting such techniques to correspondence matching in bathymetric data. We train our framework on real bathymetry from an AUV mission and evaluate its performance on the tasks of loop closure detection and coarse point cloud alignment. Finally, we show its potential against a more traditional method and release both its implementation and the dataset used

Microscopic changing-mass model of PVA hydrogel under unidirectional compression

PVA (Polyvinyl Alcohol) hydrogel is a kind of soft materials, with nontoxic and good biocompatibility. This kind of hydrogel shows strong nonlinearity in the static compression tests because of the high water content (~60‑90%), and the water is squeezed out when the PVA hydrogel is compressed. It means that the PVA hydrogel losts the mass during compression. Aiming at the changing mass mechanism and constitution in the compression of PVA hydrogel, we established a simplified microscopic model which is a single layer frame composite structure consisting of PVA fibers, water, and virtual membrane, in which the membrane wraps outside surfaces of the cubic cells and has no mass, but its membrane force has same properties with the surface tension of water. In the model, the deformation of PVA fibers and the compressive water sustains the external stress when the PVA hydrogel is compressed. In addition, by considering the limitation of the maximum membrane force induced by compressive water, the squeezed water will be calculated in each compressive step and the mechanism of the changing mass is determined quantitatively; in the meantime, the constitution of the PVA hydrogel may be deduced

Evaluation of a Canonical Image Representation for Sidescan Sonar

Acoustic sensors play an important role in autonomous underwater vehicles (AUVs). Sidescan sonar (SSS) detects a wide range and provides photo-realistic images in high resolution. However, SSS projects the 3D seafloor to 2D images, which are distorted by the AUV's altitude, target's range and sensor's resolution. As a result, the same physical area can show significant visual differences in SSS images from different survey lines, causing difficulties in tasks such as pixel correspondence and template matching. In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion. The intensity correction includes beam pattern correction and incident angle correction using three different Lambertian laws (cos, cos2, cot), whereas the slant range correction removes the nadir zone and projects the position of SSS elements into equally horizontally spaced, view-point independent bins. The proposed method is evaluated on real data collected by a HUGIN AUV, with manually-annotated pixel correspondence as ground truth reference. Experimental results on patch pairs compare similarity measures and keypoint descriptor matching. The results show that the canonical transformation can improve the patch similarity, as well as SIFT descriptor matching accuracy in different images where the same physical area was ensonified.Comment: 7 pages, 8 figure

Optimal Status Update for Caching Enabled IoT Networks: A Dueling Deep R-Network Approach

In the Internet of Things (IoT) networks, caching is a promising technique to alleviate energy consumption of sensors by responding to users' data requests with the data packets cached in the edge caching node (ECN). However, without an efficient status update strategy, the information obtained by users may be stale, which in return would inevitably deteriorate the accuracy and reliability of derived decisions for real-time applications. In this paper, we focus on striking the balance between the information freshness, in terms of age of information (AoI), experienced by users and energy consumed by sensors, by appropriately activating sensors to update their current status. Particularly, we first depict the evolutions of the AoI with each sensor from different users' perspective with time steps of non-uniform duration, which are determined by both the users' data requests and the ECN's status update decision. Then, we formulate a non-uniform time step based dynamic status update optimization problem to minimize the long-term average cost, jointly considering the average AoI and energy consumption. To this end, a Markov Decision Process is formulated and further, a dueling deep R-network based dynamic status update algorithm is devised by combining dueling deep Q-network and tabular R-learning, with which challenges from the curse of dimensionality and unknown of the environmental dynamics can be addressed. Finally, extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with five baseline deep reinforcement learning algorithms and policies

Role of magnesium-bearing silicates in the flotation of pyrite in the presence of serpentine slimes

Flotation is the most effective separation solution used in sulphide ore beneficiation. In sulphide ore flotation, the interaction between the valuable sulphide minerals and the gangues are complex. Serpentine, a common magnesium-bearing silicate mineral in sulphide ores, can largely depress the flotation of the valuable sulphide minerals by adhering at their surfaces (i.e. slime-coating). In contrast, quartz can mitigate the depressing of the valuable minerals by serpentine. This work studied the effect of two common magnesium-bearing silicate minerals in sulphide ores (i.e. pyroxene and olivine) on the flotation of pyrite which was used as a model sulphide mineral. It was found that, similar to quartz, pyroxene and olivine could significantly improve the recovery of pyrite depressed by serpentine. Zeta potential measurements and turbidity experiments showed that serpentine could aggregate with pyroxene and olivine in aqueous solution via electrostatic interaction. Furthermore, DLVO calculation revealed that serpentine preferentially interacted with pyroxene and olivine rather than pyrite, resulting in increased pyrite recovery by stripping serpentine from pyrite surface

Facile fabrication of multi-hydrogen bond self-assembly poly(Maac-co-maam) hydrogel modified pvdf ultrafiltration membrane to enhance anti-fouling property

In this work, a facile preparation method was proposed to reduce natural organics fouling of hydrophobic membrane via UV grafting polymerization with methacrylic acid (MAAc) and methyl acrylamide (MAAm) as hydrophilic monomers, followed by multihydrogen bond self-assembly. The resulting poly(vinylidene fluoride)-membranes were characterized with respect to monomer ratio, chemical structure and morphology, surface potential, and water contact angle, as well as water flux and organic foulants ultrafiltration property. The results indicated that the optimal membrane modified with a poly(MAAc-co-MAAm) polymer gel layer derived from a 1:1 monomer ratio exhibited superior hydrophilicity and excellent gel layer stability, even after ultrasonic treatment or soaking in acid or alkaline aqueous solution. The initial water contact angle of modified membranes was only 36.6° ± 2.9, and dropped to 0° within 13 s. Moreover, flux recovery rates (FRR) of modified membranes tested by bovine serum albumin (BSA), humic acid (HA), and sodium alginate (SA) solution, respectively, were all above 90% after one-cycle filtration (2 h), significantly higher than that of the pure membrane (70–76%). The total fouling rates (R$_{t}$) of the pure membrane for three foulants were as high as 47.8–56.2%, while the Rt values for modified membranes were less than 30.8%. Where R$_{t}$ of BSA dynamic filtration was merely 10.7%. The membrane designed through grafting a thin-layer hydrophilic hydrogel possessed a robust antifouling property and stability, which offers new insights for applications in pure water treatment or protein purificatio