A Robust Approach for Monocular Visual Odometry in Underwater Environments

This work presents a visual odometric system for camera tracking in underwater scenarios of the seafloor which are strongly perturbed with sunlight caustics and cloudy water. Particularly, we focuse on the performance and robustnes of the system, which structurally associates a deflickering filter with a visual tracker. Two state-of-the-art trackers are employed for our study, one pixel-oriented and the other feature-based. The contrivances of the trackers were crumbled and their suitability for underwater environments analyzed comparatively. To this end real subaquatic footages in perturbed environments were employed.Sociedad Argentina de Informática e Investigación Operativ

A Robust Approach for Monocular Visual Odometry in Underwater Environments

This work presents a visual odometric system for camera tracking in underwater scenarios of the seafloor which are strongly perturbed with sunlight caustics and cloudy water. Particularly, we focuse on the performance and robustnes of the system, which structurally associates a deflickering filter with a visual tracker. Two state-of-the-art trackers are employed for our study, one pixel-oriented and the other feature-based. The contrivances of the trackers were crumbled and their suitability for underwater environments analyzed comparatively. To this end real subaquatic footages in perturbed environments were employed.Sociedad Argentina de Informática e Investigación Operativ

Multi-sensor Mapping in natural environment: Three-Dimensional Reconstruction and temporal alignment

The objective of this thesis is the adaptation and development of robotic techniques, suitable for geometric three dimensional reconstruction of natural environments, leading into the temporal alignment of natural outdoor surveys. The objective has been achieved by adapting the state-of-the-art in field robotics and computer vision, such as sensor fusion and visual \acrfull{SLAM}. Throughout this thesis, we combine data generated by cameras, lasers and an inertial measurement unit, in order to geometrically reconstruct the surrounding scene as well as to estimate the trajectory. By supporting cameras with laser depth information, we show that it is possible to stabilize the state-of-the-art in visual odometry, and recover scale for visual maps. We also show that factor graphs are powerful tools for sensor fusion, and can be used for a generalized approach involving multiple sensors. Using semantic knowledge, we constrain the \acrfull{ICP} in order to build keyframes as well as to align them both spatially and temporally. Hierarchical clustering of ICP-generated transformations is then used to both eliminate outliers and find alignment consensus, followed by an optimization scheme based on a factor graph that includes loop closure. Data was captured using a portable robotic sensor suite consisting of three cameras, three dimensional lidar, and an inertial navigation system. Throughout this thesis, data was captured in the natural environment using a wearable sensor suite, conceived in the first months of this thesis. The data was acquired in monthly intervals over 12 months, by revisiting the same trajectory between August 2020 and July 2021. Finally, it has been shown that it is possible to align monthly surveys, taken over a year using the conceived sensor suite, and to provide insightful metrics for change evaluation in natural environment.Ph.D

Survey of Monocular SLAM Algorithms in Natural Environments

International audienceWith the increased use of cameras in robotic applications, this paper presents quantitative and qualitative assessment of the most prominent monocular tracking and mapping algorithms in literature with a particular focus on natural environments. This paper is unique in both context and methodology since it quantifies the performance of the state-of-the-art in Visual Simultaneous Localization and Mapping methods in the specific context where images mostly include vegetation. Finally, we elaborate on the limitations of these algorithms and the challenges that they did not address or consider when working in the natural environment