Non-Rigid Structure from Motion for Complex Motion

Recovering deformable 3D motion from temporal 2D point tracks in a monocular video is an open problem with many everyday applications throughout science and industry, or the new augmented reality. Recently, several techniques have been proposed to deal the problem called Non-Rigid Structure from Motion (NRSfM), however, they can exhibit poor reconstruction performance on complex motion. In this project, we will analyze these situations for primitive human actions such as walk, run, sit, jump, etc. on different scenarios, reviewing first the current techniques to finally present our novel method. This approach is able to model complex motion into a union of subspaces, rather than the summation occurring in standard low-rank shape methods, allowing better reconstruction accuracy. Experiments in a wide range of sequences and types of motion illustrate the benefits of this new approac

Grasping, Perching, And Visual Servoing For Micro Aerial Vehicles

Micro Aerial Vehicles (MAVs) have seen a dramatic growth in the consumer market because of their ability to provide new vantage points for aerial photography and videography. However, there is little consideration for physical interaction with the environment surrounding them. Onboard manipulators are absent, and onboard perception, if existent, is used to avoid obstacles and maintain a minimum distance from them. There are many applications, however, which would benefit greatly from aerial manipulation or flight in close proximity to structures. This work is focused on facilitating these types of close interactions between quadrotors and surrounding objects. We first explore high-speed grasping, enabling a quadrotor to quickly grasp an object while moving at a high relative velocity. Next, we discuss planning and control strategies, empowering a quadrotor to perch on vertical surfaces using a downward-facing gripper. Then, we demonstrate that such interactions can be achieved using only onboard sensors by incorporating vision-based control and vision-based planning. In particular, we show how a quadrotor can use a single camera and an Inertial Measurement Unit (IMU) to perch on a cylinder. Finally, we generalize our approach to consider objects in motion, and we present relative pose estimation and planning, enabling tracking of a moving sphere using only an onboard camera and IMU

Perceiving and Knowing as Activities

According to the tradition of most empiricists, perception is the basis for all our knowledge (at least of the world). The tradition also assumes that perception by humans is a passive activity resulting in some static states pertaining perception and belief, which are then, in some versions, modified by the mind before being passed onto memory and knowledge. Following the work of J. J. Gibson, we argue that perceiving involves many activities and actions. This is true of both visual as well as olfactory-taste perception. The main moral of this paper is that perceiving and knowing are best thought of not as involving static states, but rather as ongoing temporal activities involving change. This presumably means giving up a frozen ontology of states and moving towards something like a dynamic ontology as a basis