General information on the IRI research seminars


Seminars Jan - Jul 2017


Seminar 30-3-2017

Speaker: Angel Santamaria ()
Title: Trajectory Generation for Unmanned Aerial Manipulators through Quadratic Programming
Abstract:

In this paper a trajectory generation approach using quadratic programming is described for aerial manipulation, i.e. for the control of an aerial vehicle equipped with a robot arm. The proposed approach applies the online active set strategy to generate a feasible trajectory of the joints, in order to accomplish a set of tasks with defined bounds and constraint inequalities. The definition of the problem in the acceleration domain allows to integrate and perform a large set of tasks and, as a result, to obtain smooth motion of the joints. A weighting strategy, associated with a normalization procedure, allows to easily define the relative importance of the tasks. This approach is useful to accomplish different phases of a mission with different redundancy resolution strategies. The performance of the proposed technique is demonstrated through real experiments with all the algorithms running onboard in real time. In particular, the aerial manipulator can successfully perform navigation and interaction phases, while keeping motion within prescribed bounds and avoiding collisions with external obstacles.

Links: Speaker info

Seminar 20-4-2017

Speaker: Albert Pumarola ()
Title: PL-SLAM: Real-Time Monocular Visual SLAM with Points and Lines
Abstract:

Low textured scenes are well known to be one of the main Achilles heels of geometric computer vision algorithms relying on point correspondences, and in particular for visual SLAM. Yet, there are many environments in which, despite being low textured, one can still reliably estimate line-based geometric primitives, for instance in city and indoor scenes, or in the so-called Manhattan worlds, where structured edges are predominant. In this paper we propose a solution to handle these situations. Specifically, we build upon ORB-SLAM, presumably the current state-of-the-art solution both in terms of accuracy as efficiency, and extend its formulation to simultaneously handle both point and line correspondences. We propose a solution that can even work when most of the points are vanished out from the input images, and, interestingly it can be initialized from solely the detection of line correspondences in three consecutive frames. We thoroughly evaluate our approach and the new initialization strategy on the TUM RGB-D benchmark and demonstrate that the use of lines does not only improve the performance of the original ORB-SLAM solution in poorly textured frames, but also systematically improves it in sequence frames combining points and lines, without compromising the efficiency.

Links: Speaker info

Seminar 4-5-2017

Speaker: Lorenzo Porzi ()
Title: Learning Depth-aware Deep Representations for Robotic Perception
Abstract:

Exploiting RGB-D data by means of convolutional neural networks (CNNs) is at the core of a number of robotics applications, including object detection, scene semantic segmentation, and grasping. Most existing approaches, however, exploit RGB-D data by simply considering depth as an additional input channel for the network. In this paper we show that the performance of deep architectures can be boosted by introducing DaConv, a novel, general-purpose CNN block which exploits depth to learn scale-aware feature representations. We demonstrate the benefits of DaConv on a variety of robotics oriented tasks, involving affordance detection, object coordinate regression, and contour detection in RGB-D images. In each of these experiments we show the potential of the proposed block and how it can be readily integrated into existing CNN architectures.

Links: Speaker info

Seminar 19-5-2017

Speaker: Jérémie Deray ()
Title: Word Ordering and Document Adjacency for Large Loop Closure Detection in 2D Laser Maps
Abstract:

We address in this paper the problem of loop closure detection for laser-based simultaneous localization and mapping (SLAM) of very large areas. Consistent with the state of the art, the map is encoded as a graph of poses, and to cope with very large mapping capabilities, loop closures are asserted by comparing the features extracted from a query laser scan against a previously acquired corpus of scan features using a bag-ofwords (BoW) scheme. Two contributions are here presented. First, to benefit from the graph topology, feature frequency scores in the BoW are computed not only for each individual scan but also from neighboring scans in the SLAM graph. This has the effect of enforcing neighbor relational information during document matching. Secondly, a weak geometric check that takes into account feature ordering and occlusions is introduced that substantially improves loop closure detection performance. The two contributions are evaluated both separately and jointly on four common SLAM datasets, and are shown to improve the state-of-the-art performance both in terms of precision and recall in most of the cases. Moreover, our current implementation is designed to work at nearly frame rate, allowing loop closure query resolution at nearly 22 Hz for the best case scenario and 2 Hz for the worst case scenario.

Links: Speaker info