7,351 research outputs found
A Factor Graph Approach to Multi-Camera Extrinsic Calibration on Legged Robots
Legged robots are becoming popular not only in research, but also in
industry, where they can demonstrate their superiority over wheeled machines in
a variety of applications. Either when acting as mobile manipulators or just as
all-terrain ground vehicles, these machines need to precisely track the desired
base and end-effector trajectories, perform Simultaneous Localization and
Mapping (SLAM), and move in challenging environments, all while keeping
balance. A crucial aspect for these tasks is that all onboard sensors must be
properly calibrated and synchronized to provide consistent signals for all the
software modules they feed. In this paper, we focus on the problem of
calibrating the relative pose between a set of cameras and the base link of a
quadruped robot. This pose is fundamental to successfully perform sensor
fusion, state estimation, mapping, and any other task requiring visual
feedback. To solve this problem, we propose an approach based on factor graphs
that jointly optimizes the mutual position of the cameras and the robot base
using kinematics and fiducial markers. We also quantitatively compare its
performance with other state-of-the-art methods on the hydraulic quadruped
robot HyQ. The proposed approach is simple, modular, and independent from
external devices other than the fiducial marker.Comment: To appear on "The Third IEEE International Conference on Robotic
Computing (IEEE IRC 2019)
Simultaneous Parameter Calibration, Localization, and Mapping
The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters online and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real-world data using different types of robotic platforms. (C) 2012 Taylor & Francis and The Robotics Society of Japa
Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters
Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more
cameras are mounted on actuated mechanisms such as a gimbal. Existing methods
for DCC calibration rely on joint angle measurements to resolve the
time-varying transformation between the dynamic and static camera. This
information is usually provided by motor encoders, however, joint angle
measurements are not always readily available on off-the-shelf mechanisms. In
this paper, we present an encoderless approach for DCC calibration which
simultaneously estimates the kinematic parameters of the transformation chain
as well as the unknown joint angles. We also demonstrate the integration of an
encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show
the extensions required in order to perform simultaneous online estimation of
the joint angles and vehicle localization state. The proposed calibration
approach is validated both in simulation and on a physical DCC composed of a
2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the
calibrated mechanism integrated into the OKVIS VIO package, and demonstrate
successful online joint angle estimation while maintaining localization
accuracy that is comparable to a standard static multi-camera configuration.Comment: ICRA 201
The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM
New vision sensors, such as the Dynamic and Active-pixel Vision sensor
(DAVIS), incorporate a conventional global-shutter camera and an event-based
sensor in the same pixel array. These sensors have great potential for
high-speed robotics and computer vision because they allow us to combine the
benefits of conventional cameras with those of event-based sensors: low
latency, high temporal resolution, and very high dynamic range. However, new
algorithms are required to exploit the sensor characteristics and cope with its
unconventional output, which consists of a stream of asynchronous brightness
changes (called "events") and synchronous grayscale frames. For this purpose,
we present and release a collection of datasets captured with a DAVIS in a
variety of synthetic and real environments, which we hope will motivate
research on new algorithms for high-speed and high-dynamic-range robotics and
computer-vision applications. In addition to global-shutter intensity images
and asynchronous events, we provide inertial measurements and ground-truth
camera poses from a motion-capture system. The latter allows comparing the pose
accuracy of ego-motion estimation algorithms quantitatively. All the data are
released both as standard text files and binary files (i.e., rosbag). This
paper provides an overview of the available data and describes a simulator that
we release open-source to create synthetic event-camera data.Comment: 7 pages, 4 figures, 3 table
A surgical system for automatic registration, stiffness mapping and dynamic image overlay
In this paper we develop a surgical system using the da Vinci research kit
(dVRK) that is capable of autonomously searching for tumors and dynamically
displaying the tumor location using augmented reality. Such a system has the
potential to quickly reveal the location and shape of tumors and visually
overlay that information to reduce the cognitive overload of the surgeon. We
believe that our approach is one of the first to incorporate state-of-the-art
methods in registration, force sensing and tumor localization into a unified
surgical system. First, the preoperative model is registered to the
intra-operative scene using a Bingham distribution-based filtering approach. An
active level set estimation is then used to find the location and the shape of
the tumors. We use a recently developed miniature force sensor to perform the
palpation. The estimated stiffness map is then dynamically overlaid onto the
registered preoperative model of the organ. We demonstrate the efficacy of our
system by performing experiments on phantom prostate models with embedded stiff
inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018
- …