An Experimental Comparison of Monocular and Stereo Visual FastSLAM Implementations

Implementazione e confronto di FastSLAM monoculare e stereo evidenziando le differenti performances riguardo a mappatura e stima della traiettoria. E' inoltre proposto un algoritmo integrante Visual Odometry e SLAM da stereovisione per robot dotati di sole fotocamere come sensoriope

Metrological characterization of a vision-based system for relative pose measurements with fiducial marker mapping for spacecrafts

An improved approach for the measurement of the relative pose between a target and a chaser spacecraft is presented. The selected method is based on a single camera, which can be mounted on the chaser, and a plurality of fiducial markers, which can be mounted on the external surface of the target. The measurement procedure comprises of a closed-form solution of the Perspective from n Points (PnP) problem, a RANdom SAmple Consensus (RANSAC) procedure, a non-linear local optimization and a global Bundle Adjustment refinement of the marker map and relative poses. A metrological characterization of the measurement system is performed using an experimental set-up that can impose rotations combined with a linear translation and can measure them. The rotation and position measurement errors are calculated with reference instrumentations and their uncertainties are evaluated by the Monte Carlo method. The experimental laboratory tests highlight the significant improvements provided by the Bundle Adjustment refinement. Moreover, a set of possible influencing physical parameters are defined and their correlations with the rotation and position errors and uncertainties are analyzed. Using both numerical quantitative correlation coefficients and qualitative graphical representations, the most significant parameters for the final measurement errors and uncertainties are determined. The obtained results give clear indications and advice for the design of future measurement systems and for the selection of the marker positioning on a satellite surface

Simulation Framework for Mobile Robots in Planetary-Like Environments

In this paper we present a simulation framework for the evaluation of the navigation and localization metrological performances of a robotic platform. The simulator, based on ROS (Robot Operating System) Gazebo, is targeted to a planetary-like research vehicle which allows to test various perception and navigation approaches for specific environment conditions. The possibility of simulating arbitrary sensor setups comprising cameras, LiDARs (Light Detection and Ranging) and IMUs makes Gazebo an excellent resource for rapid prototyping. In this work we evaluate a variety of open-source visual and LiDAR SLAM (Simultaneous Localization and Mapping) algorithms in a simulated Martian environment. Datasets are captured by driving the rover and recording sensors outputs as well as the ground truth for a precise performance evaluation.Comment: To be presented at the 7th IEEE International Workshop on Metrology for Aerospace (MetroAerospace

Addressing the Entry Barrier for Experimentation in Perception-aware Trajectory Planning for Planetary Rovers

To fully evaluate perception-aware planning methods for planetary rover, we need a platform that takes action commands and captures perception data. Even with suitable hardware and simulation available to us, there exists an "entry barrier" for performing research in active vision, as developing methods with a system-in-the-loop is time intensive. We present our approach for tackling this entry barrier by incrementally moving from toy examples to integration and deployment on real robots. Our approach aims at reducing the overall development complexity by producing intermediate results that are used to validate and evaluate the active algorithm

Lithium ion-induced damage in silicon detectors

Silicon diodes processed by CNM on standard and oxygenated silicon substrates have been irradiated by 58 MeV lithium ions. The radiation-induced effects are very similar to the one observed after proton irradiation: substrate space charge sign inversion (SCSI), lower increase of the effective substrate doping concentration after SCSI for the oxygenated devices. The experimental radiation hardness factor has been determined to be 45.01, within 8.2% with the expected value. These results suggest that 58 MeV Li ions are a suitable radiation source for radiation hardness studies by ions heavier than protons for the future very high luminosity hadron colliders

A novel sensor for ion electron emission microscopy

Abstract An ion electron emission microscope (IEEM) to be installed at the SIRAD heavy ion irradiation facility at the 15 MV tandem accelerator of the INFN Legnaro laboratory (Italy) will be used to characterize the sensitivity of electronic devices to single event effects (SEE) to ion impacts with micrometric lateral resolutions. The secondary electrons emitted by ion impacts from the target surface are transported and focused by an electron microscope onto a micro-channel plate (MCP) detector coupled to a fast phosphor. The luminous signal is then detected by a position sensitive photon detector located outside the vacuum chamber. The high repetition rates and high spatial resolution, required to temporally distinguish ion impacts for SEE studies and avoid degrading of the initial resolution of the IEEM and MCP are met by the system, presented here for the first time, based on two orthogonal linear CCDs

Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments

The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visual place recognition approaches less effective than in urban or man-made environments. This paper presents a method to solve the loop closure problem using only spatial information. The key idea is to use a novel continuous and probabilistic representations of terrain elevation maps. Given 3D point clouds of the environment, the proposed approach exploits Gaussian Process (GP) regression with linear operators to generate continuous gradient maps of the terrain elevation information. Traditional image registration techniques are then used to search for potential matches. Loop closures are verified by leveraging both the spatial characteristic of the elevation maps (SE(2) registration) and the probabilistic nature of the GP representation. A submap-based localization and mapping framework is used to demonstrate the validity of the proposed approach. The performance of this pipeline is evaluated and benchmarked using real data from a rover that is equipped with a stereo camera and navigates in challenging, unstructured planetary-like environments in Morocco and on Mt. Etna

GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps

Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce consistent maps of an environment over time. Contrarily to urban or man-made environments, where the presence of unique objects and structures offer unique cues for localization, the apperance of unstructured natural environments is often ambiguous and self-similar, hindering the performances of loop closure detection. In this paper, we present an approach to improve the robustness of place recognition in the context of a submap-based stereo SLAM based on Gaussian Process Gradient Maps (GPGMaps). GPGMaps embed a continuous representation of the gradients of the local terrain elevation by means of Gaussian Process regression and Structured Kernel Interpolation, given solely noisy elevation measurements. We leverage the imagelike structure of GPGMaps to detect loop closures using traditional visual features and Bag of Words. GPGMap matching is performed as an SE(2) alignment to establish loop closure constraints within a pose graph. We evaluate the proposed pipeline on a variety of datasets recorded on Mt. Etna, Sicily and in the Morocco desert, respectively Moon- and Mars-like environments, and we compare the localization performances with state-of-the-art approaches for visual SLAM and visual loop closure detection

Terrain-Aware Communication Coverage Prediction for Cooperative Networked Robots in Unstructured Environments

Networked robots will play an important role in lunar exploration. Communication is key to enable cooperation among robots for information sharing, and to remotely control robots with lower degree of autonomy from a lander or habitat. Operators and scientists must be able to make sound decisions on communication availability before or during sending robots to regions of interest for exploration. In this work we have a closer look at the communication coverage prediction for lunar exploration. We present an interdisciplinary and modular framework, which exploits terrain information to predict the data rate for exploring robots. Additionally, we create intuitively usable coverage maps for operators and scientists, and show how connectivity can be improved in unstructured environments by using a relay rover. This paper provides an overview of this framework, details on individual framework components, and simulation results for two exemplary exploration scenarios

Terrain-Aware Communication Coverage Prediction for Cooperative Networked Robots in Unstructured Environments

Networked robots will play an important role in lunar exploration. Communication is key to enable cooperation among robots for information sharing, and to remotely control robots with lower degree of autonomy from a lander or habitat. Operators and scientists must be able to make sound decisions on communication availability before or during sending robots to regions of interest for exploration. In this work we have a closer look at the communication coverage prediction for lunar exploration. We present an interdisciplinary and modular framework, which exploits terrain information to predict the data rate for exploring robots. Additionally, we create intuitively usable coverage maps for operators and scientists, and show how connectivity can be improved in unstructured environments by using a relay rover. This paper provides an overview of this framework, details on individual framework components, and simulation results for two exemplary exploration scenarios