1,887 research outputs found
Shape from inconsistent silhouette: Reconstruction of objects in the presence of segmentation and camera calibration error
Silhouettes are useful features to reconstruct the object shape when the object is textureless or the shape classes of objects are unknown. In this dissertation, we explore the problem of reconstructing the shape of challenging objects from silhouettes under real-world conditions such as the presence of silhouette and camera calibration error. This problem is called the Shape from Inconsistent Silhouettes problem. A psuedo-Boolean cost function is formalized for this problem, which penalizes differences between the reconstruction images and the silhouette images, and the Shape from Inconsistent Silhouette problem is cast as a psuedo-Boolean minimization problem. We propose a memory and time efficient method to find a local minimum solution to the optimization problem, including heuristics that take into account the geometric nature of the problem. Our methods are demonstrated on a variety of challenging objects including humans and large, thin objects. We also compare our methods to the state-of-the-art by generating reconstructions of synthetic objects with induced error. ^ We also propose a method for correcting camera calibration error given silhouettes with segmentation error. Unlike other existing methods, our method allows camera calibration error to be corrected without camera placement constraints and allows for silhouette segmentation error. This is accomplished by a modified Iterative Closest Point algorithm which minimizes the difference between an initial reconstruction and the input silhouettes. We characterize the degree of error that can be corrected with synthetic datasets with increasing error, and demonstrate the ability of the camera calibration correction method in improving the reconstruction quality in several challenging real-world datasets
UAV as a Reliable Wingman: A Flight Demonstration
In this brief, we present the results from a flight experiment demonstrating two significant advances in software enabled control: optimization-based control using real-time trajectory generation and logical programming environments for formal analysis of control software. Our demonstration platform consisted of a human-piloted F-15 jet flying together with an autonomous T-33 jet. We describe the behavior of the system in two scenarios. In the first, nominal state communications were present and the autonomous aircraft maintained formation as the human pilot flew maneuvers. In the second, we imposed the loss of high-rate communications and demonstrated an autonomous safe “lost wingman” procedure to increase separation and reacquire contact. The flight demonstration included both a nominal formation flight component and an execution of the lost wingman scenario
Online learning with stability guarantees: A memory-based real-time model predictive controller
We propose and analyze a real-time model predictive control (MPC) scheme that
utilizes stored data to improve its performance by learning the value function
online with stability guarantees. For linear and nonlinear systems, a learning
method is presented that makes use of basic analytic properties of the cost
function and is proven to learn the MPC control law and the value function on
the limit set of the closed-loop state trajectory. The main idea is to generate
a smart warm start based on historical data that improves future data points
and thus future warm starts. We show that these warm starts are asymptotically
exact and converge to the solution of the MPC optimization problem. Thereby,
the suboptimality of the applied control input resulting from the real-time
requirements vanishes over time. Simulative examples show that existing
real-time MPC schemes can be improved by storing data and the proposed learning
scheme.Comment: This article is an extended version of the paper "Online learning
with stability guarantees: A memory-based warm starting for real-time MPC"
published in Automatica, Volume 122, 109247, 2020, including all proofs, an
application example, and a detailed description of the used algorith
Robust Multi-Person Tracking from Moving Platforms
In this paper, we address the problem of multi-person tracking in busy pedestrian
zones, using a stereo rig mounted on a mobile platform. The
complexity of the problem calls for an integrated solution, which
extracts as much visual information as possible and combines it
through cognitive feedback. We propose such an approach, which
jointly estimates camera position, stereo depth, object detection,
and tracking. We model the interplay between these components
using a graphical model. Since the model has to
incorporate object-object interactions, and temporal links to past
frames, direct inference is intractable. We therefore propose a two-stage
procedure: for each frame we first solve a simplified version of the
model (disregarding interactions and temporal continuity) to
estimate the scene geometry and an overcomplete set of object
detections. Conditioned on these results, we then address object
interactions, tracking, and prediction in a second step. The
approach is experimentally evaluated on several long and difficult
video sequences from busy inner-city locations. Our results show
that the proposed integration makes it possible to deliver stable
tracking performance in scenes of realistic complexity
- …