4 research outputs found
NEWTON: Neural View-Centric Mapping for On-the-Fly Large-Scale SLAM
Neural field-based 3D representations have recently been adopted in many
areas including SLAM systems. Current neural SLAM or online mapping systems
lead to impressive results in the presence of simple captures, but they rely on
a world-centric map representation as only a single neural field model is used.
To define such a world-centric representation, accurate and static prior
information about the scene, such as its boundaries and initial camera poses,
are required. However, in real-time and on-the-fly scene capture applications,
this prior knowledge cannot be assumed as fixed or static, since it dynamically
changes and it is subject to significant updates based on run-time
observations. Particularly in the context of large-scale mapping, significant
camera pose drift is inevitable, necessitating the correction via loop closure.
To overcome this limitation, we propose NEWTON, a view-centric mapping method
that dynamically constructs neural fields based on run-time observation. In
contrast to prior works, our method enables camera pose updates using loop
closures and scene boundary updates by representing the scene with multiple
neural fields, where each is defined in a local coordinate system of a selected
keyframe. The experimental results demonstrate the superior performance of our
method over existing world-centric neural field-based SLAM systems, in
particular for large-scale scenes subject to camera pose updates
Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras
We propose a novel real-time direct monocular visual odometry for
omnidirectional cameras. Our method extends direct sparse odometry (DSO) by
using the unified omnidirectional model as a projection function, which can be
applied to fisheye cameras with a field-of-view (FoV) well above 180 degrees.
This formulation allows for using the full area of the input image even with
strong distortion, while most existing visual odometry methods can only use a
rectified and cropped part of it. Model parameters within an active keyframe
window are jointly optimized, including the intrinsic/extrinsic camera
parameters, 3D position of points, and affine brightness parameters. Thanks to
the wide FoV, image overlap between frames becomes bigger and points are more
spatially distributed. Our results demonstrate that our method provides
increased accuracy and robustness over state-of-the-art visual odometry
algorithms.Comment: Accepted by IEEE Robotics and Automation Letters (RA-L), 2018 and
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
201
The magnetic field design of a compact superconducting AVF cyclotron
The superconducting magnet technology has been utilized for large-scale, high-power magnet applications for many years. It saves energy and makes magnet more compact. Application of the superconducting to a cyclotron main magnet is important and interesting, because lowcost, compact cyclotrons can be used for various applications. For examples, those shall be used in medical application, in non-destructive diagnostics of materials, in LSI fabrications, and so on. A magnetic field design of a 31 cm model superconducting AVF cyclotron is described, which can accelerate protons up to 10 MeV