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

A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation Data by Remote Sensing

Integrating the representation of the territory, through airborne remote sensing activities with hyperspectral and visible sensors, and managing complex data through dimensionality reduction for the identification of cannabis plantations, in Albania, is the focus of the research proposed by the multidisciplinary group of the Benecon University Consortium. In this study, principal components analysis (PCA) was used to remove redundant spectral information from multiband datasets. This makes it easier to identify the most prevalent spectral characteristics in most bands and those that are specific to only a few bands. The survey and airborne monitoring by hyperspectral sensors is carried out with an Itres CASI 1500 sensor owned by Benecon, characterized by a spectral range of 380–1050 nm and 288 configurable channels. The spectral configuration adopted for the research was developed specifically to maximize the spectral separability of cannabis. The ground resolution of the georeferenced cartographic data varies according to the flight planning, inserted in the aerial platform of an Italian Guardia di Finanza's aircraft, in relation to the orography of the sites under investigation. The geodatabase, wherein the processing of hyperspectral and visible images converge, contains ancillary data such as digital aeronautical maps, digital terrain models, color orthophoto, topographic data and in any case a significant amount of data so that they can be processed synergistically. The goal is to create maps and predictive scenarios, through the application of the spectral angle mapper algorithm, of the cannabis plantations scattered throughout the area. The protocol consists of comparing the spectral data acquired with the CASI1500 airborne sensor and the spectral signature of the cannabis leaves that have been acquired in the laboratory with ASD Fieldspec PRO FR spectrometers. These scientific studies have demonstrated how it is possible to achieve ex ante control of the evolution of the phenomenon itself for monitoring the cultivation of cannabis plantations

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

Nanomechanical Mapping of Hard Tissues by Atomic Force Microscopy: An Application to Cortical Bone

Force mapping of biological tissues via atomic force microscopy (AFM) probes the mechanical properties of samples within a given topography, revealing the interplay between tissue organization and nanometer-level composition. Despite considerable attention to soft biological samples, constructing elasticity maps on hard tissues is not routine for standard AFM equipment due to the difficulty of interpreting nanoindentation data in light of the available models of surface deformation. To tackle this issue, we proposed a protocol to construct elasticity maps of surfaces up to several GPa in moduli by AFM nanoindentation using standard experimental conditions (air operation, nanometrically sharp spherical tips, and cantilever stiffness below 30 N/m). We showed how to process both elastic and inelastic sample deformations simultaneously and independently and quantify the degree of elasticity of the sample to decide which regime is more suitable for moduli calculation. Afterwards, we used the frequency distributions of Young’s moduli to quantitatively assess differences between sample regions different for structure and composition, and to evaluate the presence of mechanical inhomogeneities. We tested our method on histological sections of sheep cortical bone, measuring the mechanical response of different osseous districts, and mapped the surface down to the single collagen fibril level