Learning Ground Traversability from Simulations

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots

We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. However, in robotics applications, it is often the case that (potentially very few) examples of anomalies are available. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model, by minimizing, jointly with the Real-NVP loss, an auxiliary outlier exposure margin loss. We perform quantitative experiments on a novel dataset (which we publish as supplementary material) designed for anomaly detection in an indoor patrolling scenario. On a disjoint test set, our approach outperforms alternatives and shows that exposing even a small number of anomalous frames yields significant performance improvements

Challenges in Visual Anomaly Detection for Mobile Robots

We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods. We propose a novel dataset built specifically for this task, on which we test a state-of-the-art approach; we finally discuss deployment in a real scenario.Comment: Workshop paper presented at the ICRA 2022 Workshop on Safe and Reliable Robot Autonomy under Uncertainty https://sites.google.com/umich.edu/saferobotautonomy/hom

Path planning for mobile robots in the real world: handling multiple objectives, hierarchical structures and partial information

Autonomous robots in real-world environments face a number of challenges even to accomplish apparently simple tasks like moving to a given location. We present four realistic scenarios in which robot navigation takes into account partial information, hierarchical structures, and multiple objectives. We start by discussing navigation in indoor environments shared with people, where routes are characterized by effort, risk, and social impact. Next, we improve navigation by computing optimal trajectories and implementing human-friendly local navigation behaviors. Finally, we move to outdoor environments, where robots rely on uncertain traversability estimations and need to account for the risk of getting stuck or having to change route

Intraoperative Neurophysiological Monitoring in Contemporary Spinal Surgery: A Systematic Review of Clinical Outcomes and Cost-Effectiveness

Background: Intraoperative neurophysiological monitoring (IONM) is increasingly used during spinal surgery to reduce the risk of neurological complications. This systematic review evaluates both the clinical outcomes and cost-effectiveness of IONM in contemporary spinal surgery. Methods: A comprehensive literature search was conducted to identify studies evaluating IONM in spinal surgery. Twenty-three studies were included: twenty-one reporting clinical outcomes and two focusing on economic analysis. Data on neurological deficits, monitoring accuracy, and cost-effectiveness were extracted and analyzed. Results: Analysis of the included studies showed that IONM reduced the risk of neurological deficits across various types of spinal surgery. The diagnostic accuracy varied by modality, with MEP showing the highest sensitivity (90.2%) and SSEP demonstrating high specificity (97.1%). The greatest benefit was observed in deformity surgery and spinal tumors. D-wave monitoring showed efficacy for intramedullary tumors. Economic analysis demonstrated that IONM is cost-effective when the neurological complication rate exceeds 0.3%, with potential savings of over USD 23,000 per case. Conclusions: IONM significantly improves neurological outcomes in spinal surgery and is cost-effective in most clinical scenarios, particularly in high-risk procedures. Multimodal monitoring approaches provide the most comprehensive neurological assessment. These findings support the routine use of IONM in contemporary spinal surgery, especially for complex cases

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p