Ernest Galbrun

Event-triggered tracking control of unicycle mobile robots

We investigate the stabilization of time-varying trajectories for unicycle mobile robots using event-triggered controllers. We follow an emulation-like approach in the sense that we first synthesize the controller while ignoring the communication constraints and we then derive an appropriate triggering condition. The solutions to the robot model are proved to practically converge towards the given reference trajectory, under some condition on the latter. Furthermore, the existence of a uniform minimum amount of time between any two transmissions is ensured. Afterward, experimental results are presented where the controller has been implemented on a remote computer which transmits its output to the mobile robot via an IEEE 802.11g wireless network. The proposed event-triggering strategy is able to significantly reduce the need for communication compared to a classical time-triggered setup while ensuring similar, if not better, tracking performances.

Illumination invariant optical flow using neighborhood descriptors

Optical flow approach robust towards illumination changes and texture variability.Accurate multiscale TV-l1 approach for both small and large displacements.Accurate results for very different scenes with constant algorithm parameters.High performance was obtained on the Middlebury, KITTI and MPI Sintel databases.The algorithm enabled mosaicing of endoscopic images under different modalities. Total variational (TV) methods using l1-norm are efficient approaches for optical flow determination. This contribution presents a multi-resolution TV-l1 approach using a data-term based on neighborhood descriptors and a weighted non-local regularizer. The proposed algorithm is robust to illumination changes. The benchmarking of the proposed algorithm is done with three reference databases (Middlebury, KITTI and MPI Sintel). On these databases, the proposed approach exhibits an optimal compromise between robustness, accuracy and computation speed. Numerous tests performed both on complicated data of the reference databases and on challenging endoscopic images acquired under three different modalities demonstrate the robustness and accuracy of the method against the presence of large or small displacements, weak texture information, varying illumination conditions and modality changes.

Nonlinear event-triggered tracking control of a mobile robot: design, analysis and experimental results

We consider the scenario where a controller communicates with a mobile robot via a network. Our objective is to design the control law such that the robot tracks a given reference trajectory while reducing the usage of the communication channel. For that purpose, we design a nonlinear event-triggered feedback law. We follow an emulation-like approach in the sense that we first synthesize the controller while ignoring the communication constraints and we then derive an appropriate triggering condition. We prove that the states of the robots model practically converge towards the states of the reference system which generates the desired trajectory to be tracked, using an invariance principle for hybrid systems. The existence of a dwell-time between any two transmissions is discussed. We have implemented the proposed strategy on a benchmark where the controller is located on a remote computer which communicates with the mobile robot via a IEEE 802.11g wireless network. The proposed event-triggering strategy is shown to significantly reduce the need for communication compared to a classical time-triggered setup while ensuring similar, if not better, tracking performances.

Anisotropic motion estimation on edge preserving Riesz wavelets for robust video mosaicing

Image mosaicing is a technique widely used for extending the field of view of industrial, medical, outdoor or indoor scenes. However, image registration can be very challenging, e.g. due to large texture variability, illumination changes, image blur and camera perspective changes. In this paper, a total variational optical flow approach is investigated to estimate dense point correspondences between image pairs. An edge preserving Riesz wavelet scale-space combined with a novel TV-regularizer is proposed for preserving motion discontinuities along the edges of weak textures and for handling strong in-plane rotations present in image sequences. An anisotropic weighted median filtering is implemented for minimizing outliers. Quantitative evaluation of the method on the Middlebury image database and simulated sequences with known ground truth demonstrates high accuracy of the proposed method in comparison with other state-of-the-art methods, including a robust graph-cut method and a patch matching approach. Qualitative results on video-sequences of difficult real scenes demonstrate the robustness of the proposed method. HighlightsMosaicing of images with strong texture and illumination variability.Fast, robust and accurate optical flow computation.TV-L1 method on a second order Riesz wavelet basis for preserving texture discontinuities.Novel anisotropic TV-regularizer.Accurate results for both the Middlebury data set and various complicated scenes.

Robust bladder image registration by redefining data-term in total variational approach

Cystoscopy is the standard procedure for clinical diagnosis of bladder cancer diagnosis. Bladder carcinoma in situ are often multifocal and spread over large areas. In vivo, localization and follow-up of these tumors and their nearby sites is necessary. But, due to the small field of view (FOV) of the cystoscopic video images, urologists cannot easily interpret the scene. Bladder mosaicing using image registration facilitates this interpretation through the visualization of entire lesions with respect to anatomical landmarks. The reference white light (WL) modality is affected by a strong variability in terms of texture, illumination conditions and motion blur. Moreover, in the complementary fluorescence light (FL) modality, the texture is visually different from that of the WL. Existing algorithms were developed for a particular modality and scene conditions. This paper proposes a more general on fly image registration approach for dealing with these variability issues in cystoscopy. To do so, we present a novel, robust and accurate image registration scheme by redefining the data-term of the classical total variational (TV) approach. Quantitative results on realistic bladder phantom images are used for verifying accuracy and robustness of the proposed model. This method is also qualitatively assessed with patient data mosaicing for both WL and FL modalities.

Efficiency of an accelerated Hybrid method for steady-state spatially resolved reflectance simulations in multilayered tissue-like media

This paper is devoted to the development and testing of accelerated-time calculation for the spatially resolved reflectance in multiple layer turbid medium that facilitates the use of Monte Carlo simulation (MC) in medical physics applications. To mitigate the inconveniences associated to long execution times, the MC code has been speeded up by using efficient computational hybrid technique computing on graphics processing units (GPU). This method effectively reduces the simulation time by a factor of 8 compared to the stand-alone GPU-based MC code.