Improved Fourier Mellin Invariant for Robust Rotation Estimation with Omni-cameras

Spectral methods such as the improved Fourier Mellin Invariant (iFMI) transform have proved faster, more robust and accurate than feature based methods on image registration. However, iFMI is restricted to work only when the camera moves in 2D space and has not been applied on omni-cameras images so far. In this work, we extend the iFMI method and apply a motion model to estimate an omni-camera's pose when it moves in 3D space. This is particularly useful in field robotics applications to get a rapid and comprehensive view of unstructured environments, and to estimate robustly the robot pose. In the experiment section, we compared the extended iFMI method against ORB and AKAZE feature based approaches on three datasets showing different type of environments: office, lawn and urban scenery (MPI-omni dataset). The results show that our method boosts the accuracy of the robot pose estimation two to four times with respect to the feature registration techniques, while offering lower processing times. Furthermore, the iFMI approach presents the best performance against motion blur typically present in mobile robotics.Comment: 5 pages, 4 figures, 1 tabl

DexROV: Enabling effective dexterous ROV operations in presence of communication latency

Subsea interventions in the oil & gas industry as well as in other domains such as archaeology or geological surveys are demanding and costly activities for which robotic solutions are often deployed in addition or in substitution to human divers - contributing to risks and costs cutting. The operation of ROVs (Remotely Operated Vehicles) nevertheless requires significant off-shore dedicated manpower to handle and operate the robotic platform and the supporting vessel. In order to reduce the footprint of operations, DexROV proposes to implement and evaluate novel operation paradigms with safer, more cost effective and time efficient ROV operations. As a keystone of the proposed approach, manned support will in a large extent be delocalized within an onshore ROV control center, possibly at a large distance from the actual operations, relying on satellite communications. The proposed scheme also makes provision for advanced dexterous manipulation and semi-autonomous capabilities, leveraging human expertise when deemed useful. The outcomes of the project will be integrated and evaluated in a series of tests and evaluation campaigns, culminating with a realistic deep sea (1,300 meters) trial in the Mediterranean sea

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp