Multi-modal 3D Gaussian Splatting for SLAM

From AR/VR to autonomous mobile robotics, Simultaneous Localization and Mapping (SLAM) is essential for tracking and scene understanding. 3D Gaussian Splatting (3DGS) offers a map representation capable of photorealistic reconstruction and real-time rendering of scenes using multiple posed cameras. By combining these two techniques, this thesis aims to show that a 3D Gaussian map representation is capable of accurate SLAM when given unposed RGB-D images and inertial measurements. The proposed method, MM3DGS, addresses the limitations of prior neural radiance field representations by enabling faster rendering, scale awareness, and improved trajectory tracking. In addition, a new multi-modal SLAM dataset, UT-MM, is collected from a mobile robot and is publicly released. Experimental evaluation on several scenes from the dataset shows that with the proper sensor conf iguration, MM3DGS achieves 3× improvement in tracking and 5% improvement in photometric rendering quality compared to the current 3DGS SLAM state-of-the-art, while allowing real-time rendering of a high-resolution dense 3D map.Electrical and Computer Engineerin

MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements

Simultaneous localization and mapping is essential for position tracking and scene understanding. 3D Gaussian-based map representations enable photorealistic reconstruction and real-time rendering of scenes using multiple posed cameras. We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method, MM3DGS, addresses the limitations of prior neural radiance field-based representations by enabling faster rendering, scale awareness, and improved trajectory tracking. Our framework enables keyframe-based mapping and tracking utilizing loss functions that incorporate relative pose transformations from pre-integrated inertial measurements, depth estimates, and measures of photometric rendering quality. We also release a multi-modal dataset, UT-MM, collected from a mobile robot equipped with a camera and an inertial measurement unit. Experimental evaluation on several scenes from the dataset shows that MM3DGS achieves 3x improvement in tracking and 5% improvement in photometric rendering quality compared to the current 3DGS SLAM state-of-the-art, while allowing real-time rendering of a high-resolution dense 3D map. Project Webpage: https://vita-group.github.io/MM3DGS-SLAMComment: Project Webpage: https://vita-group.github.io/MM3DGS-SLA

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Control of the Particle Distribution in Inkjet Printing through an Evaporation-Driven Sol-Gel Transition

A ring stain is often an undesirable consequence of droplet drying. Particles inside evaporating droplets with a pinned contact line are transported toward the periphery by radial flow. In this paper, we demonstrate how suspensions of laponite can be used to control the radial flow inside picoliter droplets and produce uniform deposits. The improvement in homogeneity arises from a sol–gel transition during evaporation. Droplets gel from the contact line inward, reducing the radial motion of particles and thus inhibiting the formation of a ring stain. The internal flows and propagation of the gelling front were followed by high-speed imaging of tracer particles during evaporation of the picoliter droplets of water. In the inkjet nozzle, the laponite network is broken down under high shear. Recovery of the low shear viscosity of laponite suspensions was shown to be fast with respect to the lifetime of the droplet, which was instrumental in controlling the deposit morphology. The radial and vertical particle distributions within dried deposits were measured for water droplets loaded with 1 and 5 wt % polystyrene spheres and various concentrations of laponite. Aggregation of the polystyrene spheres was suppressed by the addition of colloidal silica. The formulation can be tuned to vary the deposit profile from a ring to a pancake or a dome