30 research outputs found
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
Achieving the right balance between planning quality, safety and efficiency
is a major challenge for autonomous driving. Optimisation-based motion planners
are capable of producing safe, smooth and comfortable plans, but often at the
cost of runtime efficiency. On the other hand, naively deploying trajectories
produced by efficient-to-run deep imitation learning approaches might risk
compromising safety. In this paper, we present PILOT -- a planning framework
that comprises an imitation neural network followed by an efficient optimiser
that actively rectifies the network's plan, guaranteeing fulfilment of safety
and comfort requirements. The objective of the efficient optimiser is the same
as the objective of an expensive-to-run optimisation-based planning system that
the neural network is trained offline to imitate. This efficient optimiser
provides a key layer of online protection from learning failures or deficiency
on out-of-distribution situations that might compromise safety or comfort.
Using a state-of-the-art, runtime-intensive optimisation-based method as the
expert, we demonstrate in simulated autonomous driving experiments in CARLA
that PILOT achieves a significant reduction in runtime when compared to the
expert it imitates without sacrificing planning quality.Comment: 8 pages, 7 figure
Knowledge Capture in CMM Inspection Planning: Barriers and Challenges
Coordinate Measuring Machines (CMM) have been widely used as a means of evaluating product quality and controlling quality manufacturing processes. Many techniques have been developed to facilitate the generation of CMM measurement plans. However, there are major gaps in the understanding of planning such strategies. This significant lack of explicitly available knowledge on how experts prepare plans and carry out measurements slows down the planning process, leading to the repetitive reinvention of new plans while preventing the automation or even semi-automation of the process. The objectives of this paper are twofold: (i) to provide a review of the existing inspection planning systems and discuss the barriers and challenges, especially from the aspect of knowledge capture and formalization; and (ii) to propose and demonstrate a novel digital engineering mixed reality paradigm which has the potential to facilitate the rapid capture of implicit inspection knowledge and explicitly represent this in a formalized way. An outline and the results of the development of an early stage prototype - which will form the foundation of a more complex system to address the aforementioned technological challenges identified in the literature survey - will be given
Algorithms for on-line image registration from multiple views
Images of a scene, static or dynamic, are generally acquired at different epochs from different viewpoints. They potentially gather information about the whole scene and its relative motion with respect to the acquisition device. Data from different (in the spatial or temporal domain) visual
sources can be fused together to provide a unique consistent representation of the whole scene, even recovering the third dimension, permitting a more complete understanding of the scene content. Moreover, the pose of the acquisition device can be achieved by estimating the relative motion parameters linking different views, thus providing localization information for automatic guidance purposes. Image registration is based on the use of pattern recognition techniques to match among corresponding parts of different views of the acquired scene. Depending on hypotheses or prior information about the sensor model, the motion model and/or the scene model, this information can be used to estimate global or local geometrical mapping functions between different images or different parts of them. These mapping functions contain relative motion parameters between the scene and the sensor(s) and can be used to integrate accordingly informations coming from the different sources to build a wider or even augmented representation of the scene. Accordingly, for their scene reconstruction and pose estimation capabilities, nowadays image registration techniques from multiple views are increasingly stirring up the interest of the scientific and industrial community. Depending on the applicative domain, accuracy, robustness, and computational payload of the algorithms represent important issues to be addressed and generally a trade-off among them has to be reached. Moreover, on-line performance is desirable in order to guarantee the direct interaction of the vision device with human actors or control systems. This thesis follows a general research approach to cope with these issues, almost independently from the scene content, under the constraint of rigid motions. This approach has been motivated by the portability to very different domains as a very desirable property to achieve. A general image registration approach suitable for on-line applications has been devised and assessed through two challenging case studies in different applicative domains. The first case study regards scene reconstruction through on-line mosaicing of optical microscopy cell images acquired with non automated equipment, while moving manually the microscope holder. By registering the images the field of view of the microscope can be widened, preserving the resolution while reconstructing the whole cell culture and permitting the microscopist to interactively explore the cell culture. In the second case study, the registration of terrestrial satellite images acquired by a camera integral with the satellite is utilized to estimate its three-dimensional orientation from visual data, for automatic guidance purposes. Critical aspects of these applications are emphasized and the choices adopted are motivated accordingly. Results are discussed in view of promising future developments
An incremental method for mosaicing of optical microscope imagery
Digital imaging is nowadays widely employed in
the field of optical microscopy. One of the most apparent
benefits consists in the possibility for the researcher to see the
whole biological sample in one image, achieved by collecting
all the parts being inspected. Common approaches work in
batch mode and rely on known motorized x-y stage offsets
of the microscope holder. Or alternatively, the methods are
conceived just to provide visually pleasant mosaics off-line, that
are often built by altering the photometric values or the geometric
properties of the original component images. This work presents
an incremental mosaicing method for optical microscopy imagery,
compliant with on-line requirements and suitable even for nonmotorized
microscopes. The resulting mosaics are very accurate
and preserve the consistency of the original images so to be
used for further global measurement steps. Nevertheless, the
mosaics are visually pleasant so to be used for visual inspection
as well. The experimental results obtained in different biological
examinations confirm the efficacy of our approach