19 accepted papers
While humans can successfully navigate using abstractions, ignoring details that are irrelevant to the task at hand, most of the existing approaches in robotics require detailed environment representations which consume a significant amount of sensing, computing, and storage; these issues become par
Deep learning methods are widely used in robotic applications. By learning from prior experience, the robot can abstract knowledge of the environment, and use this knowledge to accomplish different goals, such as object search, frontier exploration, or scene understanding, with a smaller amount of r…
We introduce a novel method to estimate the essential matrix for two-view Structure from Motion (SfM). We show that every 3 by 3 essential matrix can be embedded in a 4 by 4 rotation, having its bottom right entry fixed to zero; we call the latter the quintessential matrix. This embedding leads to r…
We propose a co-navigation algorithm that enables a human and a robot to work together to navigate to a common goal. In this system, the human is responsible for making high-level steering decisions, and the robot, in turn, provides haptic feedback for collision avoidance and path suggestions while…
We tackle the problem of tracking the human lower body as an initial step toward an automatic motion assessment system for clinical mobility evaluation, using a multimodal system that combines Inertial Measurement Unit (IMU) data, RGB images, and point cloud depth measurements. This system applies t…
Shared autonomy teleoperation can guarantee safety, but does so by reducing the human operator’s control authority, which can lead to reduced levels of human-robot agreement and user satisfaction. This paper presents a novel haptic shared autonomy teleoperation paradigm that uses haptic feedback to…
We propose a novel approach for sampling-based and control-based motion planning. We combine a representation of the environment obtained via a modified version of optimal Rapidly-exploring Random Trees (RRT*), with landmark-based output-feedback controllers obtained via Control Lyapunov Functions,…
Applications of micro unmanned aerial vehicles (UAVs) are gradually expanding into complex urban and natural environments. Despite noticeable progress, flying robots in obstacle-rich environments is still challenging. On-board processing for detecting and avoiding obstacles is possible, but at a sig…
We present a novel haptic teleoperation approach that considers not only the safety but also the stability of a teleoperation system. Specifically, we build upon previous work on haptic shared control, which generates a reference haptic feedback that helps the human operator to safely navigate the r…
Convolutional neural networks applied to video streams often suffer from short-lived misclassifications or false alarms from clutter and noise. We introduce a novel network training method based on the Siamese Networks technique that mitigates false alarms in an Hourglass CNN that segments out quadc
We propose a novel system to track human lower-body motion as part of a larger movement assessment system for clinical evaluation. Our system combines multiple wearable Inertial Measurement Unit (IMU) sensors and a single external RGB-D camera. We use a factor graph with a Sliding Window Filter (SWF…
We consider the problem of enhanced security of multi-robot systems to prevent cyber-attackers from taking control of one or more robots in the group. We build upon a recently proposed solution that utilizes the physical measurement capabilities of the robots to perform introspection, i.e., detect t…
In the last few years, there has been an increasing trend to consider Structure from Motion (SfM, in computer vision) and Simultaneous Localization and Mapping (SLAM, in robotics) problems from the point of view of pose averaging (also known as global SfM, in computer vision) or Pose Graph Optimizat…
This paper considers the combination of temporal logic (TL) specifications and local objective functions to create online, multiagent, motion plans. These plans are guaranteed to satisfy a persistent mission TL specification and locally optimize an objective function (e.g. in this paper, a cost base…
Most of the current solutions for autonomous flights in indoor environments rely on purely geometric maps (e.g., point clouds). There has been, however, a growing interest in supplementing such maps with semantic information (e.g., object detections) using computer vision algorithms. Unfortunately,…
We consider the problem of localization in multiagent formations with bearing only measurements, and analyze the fundamental observability properties for dynamic agents. The current well-established approach is based on the socalled rigidity matrix, and its algebraic properties (e.g., its rank and n…
We consider the problem of finding consistent matches across multiple images. Current state-of-the-art solutions use constraints on cycles of matches together with convex optimization, leading to computationally intensive iterative algorithms. In this paper, we instead propose a clustering-based for…
The trifocal tensor, which describes the relation between projections of points and lines in three views, is a fundamental entity of geometric computer vision. In this work, we investigate a new parametrization of the trifocal tensor for calibrated cameras with non-colinear pinholes obtained from a…
Pose graph optimization is the non-convex optimization problem underlying pose-based Simultaneous Localization and Mapping (SLAM). If robot orientations were known, pose graph optimization would be a linear least-squares problem, whose solution can be computed efficiently and reliably. Since rotatio…