Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots

A slip-aware localization framework is proposed for mobile robots experiencing wheel slip in dynamic environments. The framework fuses infrastructure-aided visual tracking data (via fisheye lenses) and proprioceptive sensory data from a skid-steer mobile robot to enhance accuracy and reduce variance of the estimated states. The slip-aware localization framework includes: the visual thread to detect and track the robot in the stereo image through computationally efficient 3D point cloud generation using a region of interest; and the ego motion thread which uses a slip-aware odometry mechanism to estimate the robot pose utilizing a motion model considering wheel slip. Covariance intersection is used to fuse the pose prediction (using proprioceptive data) and the visual thread, such that the updated estimate remains consistent. As confirmed by experiments on a skid-steer mobile robot, the designed localization framework addresses state estimation challenges for indoor/outdoor autonomous mobile robots which experience high-slip, uneven torque distribution at each wheel (by the motion planner), or occlusion when observed by an infrastructure-mounted camera. The proposed system is real-time capable and scalable to multiple robots and multiple environmental cameras

Socially and Spatially Aware Motion Prediction of Dynamic Objects for Autonomous Driving

The primary goal of this thesis project is to develop a robust object motion prediction framework enabling safe decision making for autonomous vehicles in various driving scenarios. Given the comparatively higher importance and complexity of urban driving settings such as stop-sign controlled intersections or non-signalized/sign controlled roads are of primary interest; the approach, however, is not limited to these settings and is applicable to other driving settings. Specifically, motion prediction for all moving objects surrounding the autonomous vehicle such as pedestrians, cyclists, cars, and trucks is considered. In this thesis, estimation of the position and velocity of the objects surrounding the autonomous vehicle is performed using observed positions of the object in interest during a finite time window in the past, subsequent to which a socially and spatially informed model predicts the positions of these objects for a finite time window in the future through the use of the obtained position and velocity estimate as well as an artificial potential field (PF) modelling social interactions between surrounding objects and the scene. The necessary inputs for prediction are the class, position, and velocity of object of interest which can be obtained through 3D object detection approaches. However, often times, intermittent noise and/or loss in detections is observed pointing to the need for a robust estimation scheme. Traditional one-step lookback-based filtering and estimation approaches do not perform well due to a lack of sufficient prior information and simplistic model assumptions. On the other hand, most data-driven approaches do not offer any explicit embeddings of physical motion models or constraints leading to lack of generalizability in unseen scenarios. To this end, a constrained moving horizon state estimation (MHE) approach to estimate an object's states with respect to a global stationary frame including position, velocity, and acceleration that are robust to intermittently noisy or absent sensor measurements is proposed. Utilizing a computationally light-weight fusion of a Convolutional Neural Network (CNN)-based 2D object detection algorithm and projected LIDAR depth measurements, the approach produces the required measurements relative to the vehicle frame and combines them with the rotation and translation information obtained via a global positioning and inertial measurement system. The performance of the proposed approach is experimentally verified on an in-house dataset featuring urban crossings, with and without autonomous vehicle motion. Taking the position and velocity estimates as inputs, three key observations in microscopic agent-agent behaviour are incorporated for motion prediction namely – inclination to maintain direction of heading for pedestrians and follow lane centers for vehicles when free of surrounding agents, tendency to maintain heading and speed unless a collision is anticipated, and most importantly social interaction demonstrating collision avoidance. Traditionally, a fixed model or a model chosen from a fixed set of models is used for modelling future behaviour. These models are applicable to a variety of scenarios, however, they have an inherent bias and may lead to inaccurate predictions. On the other hand, purely data driven approaches suffer from a lack of holistic set of rules governing predictions and hence do not generalize well to a variety of scenarios. To address these issues, a novel potential field-based model predictive control (MPC) algorithm, MPC-PF, is proposed incorporating social interaction in a single cost function. Simulation results on a variety of scenarios including pedestrians and vehicles approaching directly head-on or otherwise show accurate predictions for a long future horizon. Furthermore, detailed qualitative and quantitative evaluation on a large public motion prediction dataset demonstrates state-of-the-art performance achieved by the proposed approach. Lastly, the potential field-based notion is integrated in a hybrid data driven Deep Deterministic Policy Gradient (DDPG) reinforcement learning (RL) agent, termed RL-PF, with a reward function governed by the potential field and is a valuable direction for further research and experimental validation

Fine-Tuning Language Models Using Formal Methods Feedback

Although pre-trained language models encode generic knowledge beneficial for planning and control, they may fail to generate appropriate control policies for domain-specific tasks. Existing fine-tuning methods use human feedback to address this limitation, however, sourcing human feedback is labor intensive and costly. We present a fully automated approach to fine-tune pre-trained language models for applications in autonomous systems, bridging the gap between generic knowledge and domain-specific requirements while reducing cost. The method synthesizes automaton-based controllers from pre-trained models guided by natural language task descriptions. These controllers are verifiable against independently provided specifications within a world model, which can be abstract or obtained from a high-fidelity simulator. Controllers with high compliance with the desired specifications receive higher ranks, guiding the iterative fine-tuning process. We provide quantitative evidences, primarily in autonomous driving, to demonstrate the method's effectiveness across multiple tasks. The results indicate an improvement in percentage of specifications satisfied by the controller from 60% to 90%

Stability Indicating LC-Method for Estimation of Paracetamol and Lornoxicam in Combined Dosage Form

A simple, specific and stability indicating reversed phase high performance liquid chromatographic method was developed for the simultaneous determination of paracetamol and lornoxicam in tablet dosage form. A Brownlee C-18, 5 μm column having 250×4.6 mm i.d. in isocratic mode, with mobile phase containing 0.05 M potassium dihydrogen phosphate:methanol (40:60, v/v) was used. The flow rate was 1.0 ml/min and effluents were monitored at 266 nm. The retention times of paracetamol and lornoxicam were 2.7 min and 5.1 min, respectively. The linearity for paracetamol and lornoxicam were in the range of 5–200 μg/ml and 0.08–20 μg/ml, respectively. Paracetamol and lornoxicam stock solutions were subjected to acid and alkali hydrolysis, chemical oxidation and dry heat degradation. The proposed method was validated and successfully applied to the estimation of paracetamol and lornoxicam in combined tablet dosage form

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

Trends in volumes and survival after hematopoietic cell transplantation in racial/ethnic minorities.

There has been an increase in volume as well as an improvement in overall survival (OS) after hematopoietic cell transplantation (HCT) for hematologic disorders. It is unknown if these changes have affected racial/ethnic minorities equally. In this observational study from the Center for International Blood and Marrow Transplant Research of 79 904 autologous (auto) and 65 662 allogeneic (allo) HCTs, we examined the volume and rates of change of autoHCT and alloHCT over time and trends in OS in 4 racial/ethnic groups: non-Hispanic Whites (NHWs), non-Hispanic African Americans (NHAAs), and Hispanics across 5 2-year cohorts from 2009 to 2018. Rates of change were compared using Poisson model. Adjusted and unadjusted Cox proportional hazards models examined trends in mortality in the 4 racial/ethnic groups over 5 study time periods. The rates of increase in volume were significantly higher for Hispanics and NHAAs vs NHW for both autoHCT and alloHCT. Adjusted overall mortality after autoHCT was comparable across all racial/ethnic groups. NHAA adults (hazard ratio [HR] 1.13; 95% confidence interval [CI] 1.04-1.22; P = .004) and pediatric patients (HR 1.62; 95% CI 1.3-2.03; P < .001) had a higher risk of mortality after alloHCT than NHWs. Improvement in OS over time was seen in all 4 groups after both autoHCT and alloHCT. Our study shows the rate of change for the use of autoHCT and alloHCT is higher in NHAAs and Hispanics than in NHWs. Survival after autoHCT and alloHCT improved over time; however, NHAAs have worse OS after alloHCT, which has persisted. Continued efforts are needed to mitigate disparities for patients requiring alloHCT

Trends in volumes and survival after hematopoietic cell transplantation in racial/ethnic minorities

ABSTRACT: There has been an increase in volume as well as an improvement in overall survival (OS) after hematopoietic cell transplantation (HCT) for hematologic disorders. It is unknown if these changes have affected racial/ethnic minorities equally. In this observational study from the Center for International Blood and Marrow Transplant Research of 79 904 autologous (auto) and 65 662 allogeneic (allo) HCTs, we examined the volume and rates of change of autoHCT and alloHCT over time and trends in OS in 4 racial/ethnic groups: non-Hispanic Whites (NHWs), non-Hispanic African Americans (NHAAs), and Hispanics across 5 2-year cohorts from 2009 to 2018. Rates of change were compared using Poisson model. Adjusted and unadjusted Cox proportional hazards models examined trends in mortality in the 4 racial/ethnic groups over 5 study time periods. The rates of increase in volume were significantly higher for Hispanics and NHAAs vs NHW for both autoHCT and alloHCT. Adjusted overall mortality after autoHCT was comparable across all racial/ethnic groups. NHAA adults (hazard ratio [HR] 1.13; 95% confidence interval [CI] 1.04-1.22; P = .004) and pediatric patients (HR 1.62; 95% CI 1.3-2.03; P < .001) had a higher risk of mortality after alloHCT than NHWs. Improvement in OS over time was seen in all 4 groups after both autoHCT and alloHCT. Our study shows the rate of change for the use of autoHCT and alloHCT is higher in NHAAs and Hispanics than in NHWs. Survival after autoHCT and alloHCT improved over time; however, NHAAs have worse OS after alloHCT, which has persisted. Continued efforts are needed to mitigate disparities for patients requiring alloHCT

Comparison of two strategies for the treatment of acute myocardial infarction: In-hospital outcomes analysis

A strategy of pre-hospital reduced dose fibrinolytic administration coupled with urgent coronary intervention (PCI) for patients with STEMI (FAST-PCI) has been found to be superior to primary PCI (PPCI) alone. A coordinated STEMI system-of-care that includes FAST-PCI might offer better outcomes than pre-hospital diagnosis and STEMI team activation followed by PPCI alone. We compared the in-hospital outcomes for patients treated with the FAST-PCI approach with outcomes for patients treated with the PPCI approach during a pause in the FAST-PCI protocol. In-hospital data for 253 STEMI patients (03/2003–12/2009), treated with FAST-PCI protocol were compared to 124 patients (12/2009–08/2011), treated with PPCI strategy alone. In-hospital mortality was the primary endpoint. Stroke, major bleeding, and reinfarction during index hospitalization were secondary endpoints. Comparing the strategies used during the two time intervals, in-hospital mortality was significantly lower with FAST-PCI than with PPCI (2.77% vs. 10.48%, p = 0.0017). Rates of stroke, reinfarction and major bleeding were similar between the two groups. There was a lower frequency of pre- PCI TIMI 0 flow (no patency) seen in patients treated with FAST-PCI compared to the PPCI patients (26.7% vs. 62.7%, p\u3c0.0001). Earlier infarct related artery patency in the FAST-PCI group had a favorable impact on the incidence of cardiogenic shock at hospital admission (FAST-PCI- 3.1% vs. PPCI- 20.9%, p\u3c0.0001). The FAST-PCI strategy was associated with earlier infarct related artery patency and the lower incidence of cardiogenic shock on hospital arrival, as well as with reduced in-hospital mortality among STEMI patients