9 accepted papers
Visual Odometry (VO) and Visual SLAM (VSLAM) systems often struggle in low-light and dark environments due to the lack of robust visual features. In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. Th
Autonomous robotic platforms can be effectively used to perform automatic fruits yield estimation. To this aim, robots need data-driven models that process image streams and count, even approximately, the number of fruits in an orchard. However, training such models following a supervised paradigm i
In this work, we address the problem of visual navigation by following instructions. In this task, the robot must interpret a natural language instruction in order to follow a predefined path in a possibly unknown environment. Despite different approaches have been proposed in the last years, they a
Fruit counting is a fundamental component for yield estimation applications. Most of the existing approaches address this problem by relying on fruit models (i.e., by using object detectors) or by explicitly learning to count. Despite the impressive results achieved by these approaches, all of them
Enabling effective human-robot interaction is crucial for any service robotics application. In this context, a fundamental aspect is the development of a user-friendly human-robot interface, such as a natural language interface. In this letter, we investigate the robot side of the interface, in part
In this letter, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches rely either on Visual simultaneous localization and mapping (SLAM) systems or on depth estimation models to build t
Modern autonomous mobile robots require a strong understanding of their surroundings in order to safely operate in cluttered and dynamic environments. Monocular depth estimation offers a geometry-independent paradigm to detect free, navigable space with minimum space, and power consumption. These re
Visual ego-motion estimation, or briefly visual odometry (VO), is one of the key building blocks of modern SLAM systems. In the last decade, impressive results have been demonstrated in the context of visual navigation, reaching very high localization performance. However, all ego-motion estimation
Obstacle Detection is a central problem for any robotic system, and critical for autonomous systems that travel at high speeds in unpredictable environment. This is often achieved through scene depth estimation, by various means. When fast motion is considered, the detection range must be longer eno…