Nuclear facilities often require continuous monitoring to ensure there is no contamination of radioactive materials that might lead to safety or environmental issues. The current approach to radiological monitoring is to use human operators, which is both time consuming and cost inefficient. As with many repetitive, routine tasks, there are considerable opportunities for the process to be improved using autonomous robotic systems.

This paper proposes a new approach, interval Simultaneous Localization and Mapping (i-SLAM), which addresses the robotic mapping problem in the context of interval methods, where the robot sensor noise is assumed bounded. With no prior knowledge about the noise distribution or its probability density function, we derive and present necessary conditions to guarantee the map convergence even in the presence of nonlinear observation and motion models. These conditions may require the presence of some anchoring landmarks with known locations. The performance of i-SLAM is compared with the probabilistic counterparts in terms of accuracy and efficiency.

Trabajo presentado al 10th Summer Workshop on Interval Methods, and 3rd International Symposium on Set Membership - Applications, Reliability and Theory, celebrado en Manchester (UK) del 14 al 16 de junio de 2017.

When mobile robots are intended to be used in hazardous environments or for long-time operations, it is needed to increase their robustness against faults. This could be achieved by means of the inclusion of Fault Tolerant Control (FTC) mechanisms. In this paper, a FTC based on fault hiding approach is proposed for a non-holonomic mobile robot. First, a Sliding Mode Controller (SMC) is designed to control the robost and to cope with modelling uncertainty. Later on, it is enhanced to take into account actuator faults leading to a fault hiding approach for the sliding mode fault-tolerant control of the robot. Results using simulated fault scenarios are presented to illustrate the performance of the proposed approach.

This article presents a novel systematic methodology for the detection of interest points in 3D point clouds and its corresponding descriptors by using the information of an RGB camera and a structured-light sensor. This is achieved by fusing Speeded-Up Robust Features (SURF) in the image space, and histograms that statistically represent the relationship of three dimensional geometric data around the interest points. The SURF algorithm is implemented over an image whose pixel coordinates have a direct corresponding 3D point, thus allowing the fusion of both approaches. By combining both methodologies, it is intent to define a set of interest points whose descriptors are able to maintain the intrinsic characteristics of its constituent parts such as repeatability, distinctiveness and robustness while remaining compact and fast to compute. The detected points will be use for both, localization and mapping of mobile robots in partially unknown environments.

For SLAM problem [3], building an accurate map leads to an accurate localization. We propose a guaranteed solution using interval methods for nonlinear observation model to work with holnomic robots with no rotation, where the map is proven to converge. Our approach does not require any assumptions with regard to the linearity of the observation model, nor its noise except that it needs to be bounded. We use interval methods to evaluate the domain of a function given the codomain and the function itself. This approach encapsulates all information in the current estimate, therefore, it is not necessary to keep track of all past observations. We will prove the convergence of the approach to the correct map as the robot moves in the environment over time, given that at each time step, at least one old landmark is observed, and the data association problem is assumed a solved problem.

Rigid transformation is a popular method to estimate the robot motion given two sets of corresponding points seen from two different locations. Range imaging sensors, such as stereo camera and structured-light 3-D scanner, can be used to provide these corresponding points, however, such sensors have measurement uncertainty defined by intervals with upper and lower bounds. This paper presents a new approach to use interval analysis and simultaneously estimate the following: (1) the robot motion and, (2) the position of the landmarks with respect to the initial frame of reference, i.e. the robot initial pose. We will show that using this approach, the uncertainties of the landmark positions decrease over time, which causes the uncertainty of the robot pose to remain bounded. Our approach is illustrated with examples using simulated data, and real data acquired by Kinect sensor.